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mlx-community
/
Mage-VL-8bit

Image-Text-to-Text
MLX
Safetensors
mage_vl
vision-language-model
video-understanding
mage-vl
conversational
custom_code
8-bit precision
Model card Files Files and versions
xet
Community
1

Instructions to use mlx-community/Mage-VL-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use mlx-community/Mage-VL-8bit with MLX:

    # Make sure mlx-vlm is installed
    # pip install --upgrade mlx-vlm
    
    from mlx_vlm import load, generate
    from mlx_vlm.prompt_utils import apply_chat_template
    from mlx_vlm.utils import load_config
    
    # Load the model
    model, processor = load("mlx-community/Mage-VL-8bit")
    config = load_config("mlx-community/Mage-VL-8bit")
    
    # Prepare input
    image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
    prompt = "Describe this image."
    
    # Apply chat template
    formatted_prompt = apply_chat_template(
        processor, config, prompt, num_images=1
    )
    
    # Generate output
    output = generate(model, processor, formatted_prompt, image)
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • LM Studio
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  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Following the model card's setup verbatim fails on a standard Apple-Silicon MLX env — `ones_like(): argument 'input' must be Tensor, not mlx.core.array` (workaround included)

#1 opened about 4 hours ago by
ismaelvega
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