Instructions to use Chesscorner/git-chess-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chesscorner/git-chess-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Chesscorner/git-chess-v2")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Chesscorner/git-chess-v2")
model = AutoModelForImageTextToText.from_pretrained("Chesscorner/git-chess-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Chesscorner/git-chess-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chesscorner/git-chess-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chesscorner/git-chess-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Chesscorner/git-chess-v2

SGLang

How to use Chesscorner/git-chess-v2 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 "Chesscorner/git-chess-v2" \
    --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": "Chesscorner/git-chess-v2",
		"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 "Chesscorner/git-chess-v2" \
        --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": "Chesscorner/git-chess-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Chesscorner/git-chess-v2 with Docker Model Runner:
```
docker model run hf.co/Chesscorner/git-chess-v2
```

git-chess-v2 / model.safetensors

Chesscorner

Upload GitForCausalLM

3d611de verified over 1 year ago

download

history blame contribute delete

707 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 76082ce07e99d11176a606b05b7b4e87f0e74d392ef4b87beb07168c18607328
Size of remote file:: 707 MB
SHA256:: 3c0cd4fad8743b40492e8021f4a517f08bf62a2d1bc6bae4b3b39c8e1a505cba

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.