Instructions to use submarat/gpt2-small-fineweb-edu-10b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use submarat/gpt2-small-fineweb-edu-10b-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="submarat/gpt2-small-fineweb-edu-10b-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("submarat/gpt2-small-fineweb-edu-10b-chat")
model = AutoModelForCausalLM.from_pretrained("submarat/gpt2-small-fineweb-edu-10b-chat", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use submarat/gpt2-small-fineweb-edu-10b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "submarat/gpt2-small-fineweb-edu-10b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "submarat/gpt2-small-fineweb-edu-10b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/submarat/gpt2-small-fineweb-edu-10b-chat

SGLang

How to use submarat/gpt2-small-fineweb-edu-10b-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "submarat/gpt2-small-fineweb-edu-10b-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "submarat/gpt2-small-fineweb-edu-10b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "submarat/gpt2-small-fineweb-edu-10b-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "submarat/gpt2-small-fineweb-edu-10b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use submarat/gpt2-small-fineweb-edu-10b-chat with Docker Model Runner:
```
docker model run hf.co/submarat/gpt2-small-fineweb-edu-10b-chat
```

GPT-2 Small — multi-turn chat (toy)

Multi-turn chat fine-tune of submarat/gpt2-small-fineweb-edu-10b (124M GPT-2 reproduced from scratch on 10B FineWeb-Edu tokens). SFT on smol-smoltalk (conversational, built for small models) with a ChatML-style chat template and assistant_only_loss (loss on assistant turns across the whole conversation).

Unlike the single-turn SFT model (Alpaca), this one carries a chat_template and holds a running conversation.

Interactive demo: https://huggingface.co/spaces/submarat/gpt2-fineweb-chat
Code: https://github.com/submarat/gpt2-small-repro (posttraining/sft_chat.py)
Write-up: https://submarat.github.io/a-toy-assistant-sft-and-dpo/

It's a toy: 124M with a 1024-token context, so multi-turn coherence degrades quickly and it hallucinates. It holds the chat format and can reference recent turns — don't expect real conversational memory.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

m = AutoModelForCausalLM.from_pretrained("submarat/gpt2-small-fineweb-edu-10b-chat")
tok = AutoTokenizer.from_pretrained("submarat/gpt2-small-fineweb-edu-10b-chat")

messages = [{"role": "user", "content": "Give me three tips for studying."}]
prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
ids = tok(prompt, return_tensors="pt").input_ids
out = m.generate(ids, max_new_tokens=100, do_sample=True, top_k=40, temperature=0.7,
                 repetition_penalty=1.3, pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Training

Base: submarat/gpt2-small-fineweb-edu-10b (124M)
Data: smol-smoltalk (40k conversations)
2 epochs, batch 24, LR 2e-5 cosine, bf16, ctx 1024, assistant_only_loss

Downloads last month: 51

Safetensors

Model size

0.1B params

Tensor type

F32

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Training

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