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JANGQ-AI
/
ZAYA1-VL-8B-JANGTQ4

Image-Text-to-Text
MLX
Safetensors
zaya1_vl
zaya
mixture-of-experts
hybrid-attention
cca-attention
apple-silicon
reasoning
tool-use
quantized
vision
multimodal
vision-language
qwen2_5_vl-vit
jang
jangtq
mxtq
jangtq-prestack
jangq
conversational
Model card Files Files and versions
xet
Community

Instructions to use JANGQ-AI/ZAYA1-VL-8B-JANGTQ4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use JANGQ-AI/ZAYA1-VL-8B-JANGTQ4 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("JANGQ-AI/ZAYA1-VL-8B-JANGTQ4")
    config = load_config("JANGQ-AI/ZAYA1-VL-8B-JANGTQ4")
    
    # 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
  • Atomic Chat
ZAYA1-VL-8B-JANGTQ4
1.52 kB
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  • 2 contributors
History: 1 commit
Osaurus-AI's picture
Osaurus-AI
initial commit
12ac6f0 verified 4 months ago
  • .gitattributes
    1.52 kB
    initial commit 4 months ago