Instructions to use dgrauet/void-model-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dgrauet/void-model-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir void-model-mlx dgrauet/void-model-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Add the usage snippet, taken from the inference project's own README
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split_model.json
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"extra_links": [
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"q8 variant: https://huggingface.co/dgrauet/void-model-mlx-q8",
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"q4 variant: https://huggingface.co/dgrauet/void-model-mlx-q4"
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]
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}
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"extra_links": [
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"q8 variant: https://huggingface.co/dgrauet/void-model-mlx-q8",
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"q4 variant: https://huggingface.co/dgrauet/void-model-mlx-q4"
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"cli_snippet": "git clone https://github.com/dgrauet/VideoX-Fun-mlx.git\nexport VIDEOX_FUN_MLX_PATH=/path/to/VideoX-Fun-mlx\npip install mlx opencv-python-headless pillow numpy sentencepiece\n\npython -m void_mlx.infer \\\n --sample sample/BigBen \\\n --pass1 weights/void_pass1.safetensors \\\n --pass2 weights/void_pass2.safetensors \\\n --base-model /path/to/CogVideoX-Fun-V1.5-5b-InP-mlx-q8 \\\n --steps 30 --max-frames 13 --height 352 --width 624 \\\n --low-ram --output result.gif"
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}
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