Instructions to use matthh/git-image-to-g-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matthh/git-image-to-g-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="matthh/git-image-to-g-code")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("matthh/git-image-to-g-code") model = AutoModelForImageTextToText.from_pretrained("matthh/git-image-to-g-code") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use matthh/git-image-to-g-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "matthh/git-image-to-g-code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "matthh/git-image-to-g-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/matthh/git-image-to-g-code
- SGLang
How to use matthh/git-image-to-g-code 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 "matthh/git-image-to-g-code" \ --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": "matthh/git-image-to-g-code", "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 "matthh/git-image-to-g-code" \ --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": "matthh/git-image-to-g-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use matthh/git-image-to-g-code with Docker Model Runner:
docker model run hf.co/matthh/git-image-to-g-code
- Xet hash:
- 339c93b182eeccd510ff239651fbd7999deb393ac2b07f351f95affe4e82fe81
- Size of remote file:
- 707 MB
- SHA256:
- 316f95f9c1146aea150212d164f4cb10fcd8240b233596974ad7ba1079d88e6d
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