QwQ-0.5B-Distilled-SFT

4

24

1 language

license:apache-2.0

by

kz919

Language Model

OTHER

0.5B params

New

4 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

2GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

1GB+ RAM

Code Examples

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Training Progress:pythontransformers

import os
import argparse
import torch
from datasets import Dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)
from datasets import load_dataset
from peft import LoraConfig

parser = argparse.ArgumentParser()
parser.add_argument("--max_length", type=int, default = 4096)
parser.add_argument("--output_dir", type=str, default="gkd-model")
parser.add_argument("--per_device_train_batch_size", type=int, default=1)
parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
parser.add_argument("--lora", action="store_true")
args = parser.parse_args()

qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
messages = []
for each in qwq_dataset:
    msg = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": each["problem"]},
        {"role": "assistant", "content": each["qwq"]},
    ]
    messages.append(msg)

TRAIN_SPLIT_RATIO = 0.9
train_size = int(TRAIN_SPLIT_RATIO * len(messages))
eval_size = len(messages) - train_size

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# The model to optimise
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 



### Real Dataset
train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
training_args = SFTConfig(
    output_dir=args.output_dir,
    max_seq_length=args.max_length,
    per_device_train_batch_size=args.per_device_train_batch_size,
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    gradient_checkpointing = args.gradient_checkpointing,
    save_steps = 100,
    save_total_limit = 5
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

response_template = "<|im_start|>assistant\n"

collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    processing_class=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=lora_config if args.lora else None,
    data_collator=collator,
)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Dataset:pythontransformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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