praxis-bookwriter-llama3.1-8b-sft

Name: praxis-bookwriter-llama3.1-8b-sft
Author: maldv

8.0B

1 language

llama

maldv

Language Model

OTHER

8B params

New

23 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

18GB+ RAM

Mobile

Laptop

Server

Quick Summary

My last iteration of fantasy writer suffered from one glaring flaw: It did not really follow instructions well.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

8GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by praxis-bookwriter-llama3.1-8b-sft with quality assessment

Specialized For

general

science

multilingual

reasoning

Training Datasets (4)

common crawl

🔴 2.5/10

general

science

Key Strengths

•Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
•Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
•Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...

Considerations

•Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
•Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...

🔵 6/10

general

multilingual

Key Strengths

•Scale and Accessibility: 750GB of publicly available, filtered text
•Systematic Filtering: Documented heuristics enable reproducibility
•Language Diversity: Despite English-only, captures diverse writing styles

Considerations

•English-Only: Limits multilingual applications
•Filtering Limitations: Offensive content and low-quality text remain despite filtering

wikipedia

🟡 5/10

science

multilingual

Key Strengths

•High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
•Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
•Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...

Considerations

•Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
•Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...

arxiv

🟡 5.5/10

science

reasoning

Key Strengths

•Scientific Authority: Peer-reviewed content from established repository
•Domain-Specific: Specialized vocabulary and concepts
•Mathematical Content: Includes complex equations and notation

Considerations

•Specialized: Primarily technical and mathematical content
•English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Trainingpythontransformers

from datasets import load_from_disk
from dotenv import dotenv_values
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from transformers import TrainingArguments
from trl import SFTTrainer
import wandb

envconfig = dict(dotenv_values(".env"))

dtype = None
max_seq_length = 24576
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Meta-Llama-3.1-8B",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r = 128,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 128**.5,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = True,
    loftq_config = None,
)

dataset = load_from_disk('bookdata')
ds_train = dataset
ds_eval = dataset.shuffle(seed=12345).select(range(32))

targs = TrainingArguments(
    per_device_train_batch_size = 3,
    gradient_accumulation_steps = 4,
    learning_rate = 4e-5,
    weight_decay = 0,
    gradient_checkpointing = True,
    max_grad_norm = 1,
    warmup_steps = 5,
    num_train_epochs = 3,
    optim = "paged_adamw_32bit",
    lr_scheduler_type = "cosine",
    seed = 3407,
    fp16 = not is_bfloat16_supported(),
    bf16 = is_bfloat16_supported(),
    logging_steps = 1,
    per_device_eval_batch_size = 1,
    do_eval = True,
    eval_steps = 25,
    eval_strategy = "steps",
    save_strategy = "steps",
    save_steps = 20,
    save_total_limit = 3,
    output_dir = "outputs",
    report_to="wandb",
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = ds_train,
    eval_dataset = ds_eval,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 6,
    packing = False,
    args = targs,
)

wandb.login(key=envconfig['wandb_key'])
wandb.init(
    project='bookwriter-596',
    config={
        "learning_rate": 4e-5,
        "architecture": 'llama 3.1 8b',
        "dataset": 'bookdata',
        "epochs": 3,
    }
)

#trainer_stats = trainer.train()
trainer.train(resume_from_checkpoint=True)

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.