Instructions to use YingxuHe/git-base-test4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YingxuHe/git-base-test4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="YingxuHe/git-base-test4")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("YingxuHe/git-base-test4") model = AutoModelForMultimodalLM.from_pretrained("YingxuHe/git-base-test4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use YingxuHe/git-base-test4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "YingxuHe/git-base-test4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "YingxuHe/git-base-test4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/YingxuHe/git-base-test4
- SGLang
How to use YingxuHe/git-base-test4 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 "YingxuHe/git-base-test4" \ --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": "YingxuHe/git-base-test4", "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 "YingxuHe/git-base-test4" \ --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": "YingxuHe/git-base-test4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use YingxuHe/git-base-test4 with Docker Model Runner:
docker model run hf.co/YingxuHe/git-base-test4
Download model.safetensors from YingxuHe/git-base-test4: direct link, hf CLI and curl.
- Browser
- Download file 707 MB
-
https://huggingface.co/YingxuHe/git-base-test4/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://YingxuHe/git-base-test4@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/YingxuHe/git-base-test4/resolve/refs%2Fpr%2F1/model.safetensors
707 MB
- Xet hash:
- c70de4fe1c25cbe64baa2d62f2987d24b4b59a1d4715775e8478bff159aa010c
- Size of remote file:
- 707 MB
- SHA256:
- 5ad0f074fd87f2a14acebf9f3fcef1000b2829acec61a98cb33317182f1c4986
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.