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ncoop57
/
stable_vision_coder

Image-Text-to-Text
Transformers
PyTorch
fuyu
Model card Files Files and versions
xet
Community

Instructions to use ncoop57/stable_vision_coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ncoop57/stable_vision_coder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="ncoop57/stable_vision_coder")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("ncoop57/stable_vision_coder")
    model = AutoModelForImageTextToText.from_pretrained("ncoop57/stable_vision_coder")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use ncoop57/stable_vision_coder with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "ncoop57/stable_vision_coder"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "ncoop57/stable_vision_coder",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/ncoop57/stable_vision_coder
  • SGLang

    How to use ncoop57/stable_vision_coder 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 "ncoop57/stable_vision_coder" \
        --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": "ncoop57/stable_vision_coder",
    		"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 "ncoop57/stable_vision_coder" \
            --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": "ncoop57/stable_vision_coder",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use ncoop57/stable_vision_coder with Docker Model Runner:

    docker model run hf.co/ncoop57/stable_vision_coder
stable_vision_coder
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  • 1 contributor
History: 3 commits
ncoop57's picture
ncoop57
Update config.json
d3039ca verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • .gitignore
    15 Bytes
    End of training over 2 years ago
  • added_tokens.json
    27 Bytes
    End of training over 2 years ago
  • config.json
    1.22 kB
    Update config.json over 2 years ago
  • generation_config.json
    111 Bytes
    End of training over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    4.42 GB
    xet
    End of training over 2 years ago
  • special_tokens_map.json
    909 Bytes
    End of training over 2 years ago
  • tokenizer.json
    1.84 MB
    End of training over 2 years ago
  • tokenizer.model
    500 kB
    xet
    End of training over 2 years ago
  • tokenizer_config.json
    1.36 kB
    End of training over 2 years ago