EduHelp 8B
92
5
8.0B
1 language
license:mit
by
s3nh
Language Model
OTHER
8B params
New
92 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary
EduHelper is a child-friendly tutoring assistant fine-tuned from the Qwen3-8B base model using parameter-efficient fine-tuning (PEFT) with LoRA on the ajibawa-2023/Education-Young-Children dataset.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM
Code Examples
How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))How to Usepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "s3nh/EduHelper_Qwen3_8B_6500steps"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
messages = [
{"role": "system", "content": "You are a kind and patient tutor for young children. Use simple words and a friendly tone."},
{"role": "user", "content": "Can you explain what a verb is with two examples?"}
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=200,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Deploy This Model
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