Instructions to use dgrauet/void-model-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dgrauet/void-model-mlx-q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir void-model-mlx-q4 dgrauet/void-model-mlx-q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Add int4 weights (group_size 64), config, quantize_config — repo was empty (#dogfood 2026-07-20)
Browse files- config.json +10 -0
- quantize_config.json +6 -0
- void_pass1.safetensors +3 -0
- void_pass2.safetensors +3 -0
config.json
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{
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"model_type": "void-transformer",
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"source": "netflix-void",
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"architecture": "CogVideoXTransformer3DModel",
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"passes": ["void_pass1", "void_pass2"],
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"notes": {
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"patch_embed": "Linear with in_dim=384 (in_channels=48 [16 latent + 16 VAE-mask + 16 VAE-video] * patch_volume=8).",
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"base_model": "Uses VAE and T5 from CogVideoX-Fun-V1.5-5b-InP."
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}
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}
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quantize_config.json
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{
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"quantization": {
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"bits": 4,
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"group_size": 64
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}
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}
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void_pass1.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0fdc41308cef541ce0b28630090599503359d6136a139d7085e3a56897ea599
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size 4305860459
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void_pass2.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:731ad1d807884dcf23cc7bc7e752a99cd7ace92f5a049d6d4b71a0d7a3abdcae
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size 4305860459
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