Image-Text-to-Text
MLX
Safetensors
mage_vl
vision-language-model
video-understanding
mage-vl
conversational
custom_code
8-bit precision
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
- Atomic Chat
Commit History
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PR #1745 merged: install from upstream mlx-vlm, retire the fork branch reference
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Correct the activation figure: 14 GB is the 8192-token budget, not the 4096 default
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Card: torch-free install note (discussion #1, fixed in branch bf9ca87)
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Card: link mlx-vlm PR #1745; verified snippet, no trust_remote_code needed
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Mage-VL 8-bit (mlx_vlm affine g64, 9.023 bpw) — 48/48 greedy-token parity with the PyTorch reference
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initial commit
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