Instructions to use gitcoreai/gpt-2-small-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gitcoreai/gpt-2-small-sft with Transformers:

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

pipe = pipeline("text-generation", model="gitcoreai/gpt-2-small-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gitcoreai/gpt-2-small-sft")
model = AutoModelForCausalLM.from_pretrained("gitcoreai/gpt-2-small-sft", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gitcoreai/gpt-2-small-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gitcoreai/gpt-2-small-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gitcoreai/gpt-2-small-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gitcoreai/gpt-2-small-sft

SGLang

How to use gitcoreai/gpt-2-small-sft 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 "gitcoreai/gpt-2-small-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gitcoreai/gpt-2-small-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "gitcoreai/gpt-2-small-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gitcoreai/gpt-2-small-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gitcoreai/gpt-2-small-sft with Docker Model Runner:
```
docker model run hf.co/gitcoreai/gpt-2-small-sft
```

gpt-2-small-sft / README.md

gitcoreai

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	---
	license: mit
	base_model: openai-community/gpt2
	datasets:
	- OpenAssistant/oasst1
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- gpt2
	- gpt-2
	- chatbot
	- text-generation-inference
	---

	# GPT-2 SFT on OASST1

	This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset.

	## Training Procedure

	- Base Model: GPT-2 Small (124M parameters)
	- Dataset: OpenAssistant OASST1 (English only)
	- Format: `user: ... assistant: <s> ... </s>`
	- Epochs: 15
	- Learning Rate: 5e-5
	- Batch Size: 4 (effective 16 with gradient accumulation)

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_name = "gitcoreai/gpt-2-small-sft"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	prompt = "user: hello, how are you?\nassistant: <s>"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=60)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))