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
| { | |
| "format": "split", | |
| "components": [ | |
| "void_pass1", | |
| "void_pass2" | |
| ], | |
| "quantized": true, | |
| "quantization_bits": 4, | |
| "quantization_group_size": 64, | |
| "recipe": "void-model", | |
| "source": "netflix/void-model", | |
| "license": "apache-2.0", | |
| "quantization_scope": "transformer Linear weights only", | |
| "links": [ | |
| "void-model-mlx (inference): https://github.com/dgrauet/void-model-mlx", | |
| "VideoX-Fun-mlx (engine): https://github.com/dgrauet/VideoX-Fun-mlx" | |
| ], | |
| "usage_url": "https://github.com/dgrauet/void-model-mlx", | |
| "extra_links": [ | |
| "Base model weights (q8): https://huggingface.co/dgrauet/CogVideoX-Fun-V1.5-5b-InP-mlx-q8", | |
| "bf16 variant: https://huggingface.co/dgrauet/void-model-mlx", | |
| "q8 variant: https://huggingface.co/dgrauet/void-model-mlx-q8" | |
| ], | |
| "cli_snippet": "python -m void_mlx.infer \\\n --sample sample/BigBen \\\n --pass1 weights/q4/void_pass1.safetensors \\\n --pass2 weights/q4/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 --output result.gif", | |
| "build_note": "**This is the 32 GB configuration**: paired with the q8 base model, a full\ntwo-pass BigBen run (30 steps, 13 frames, 352\u00d7624) peaks at ~23.7 GB \u2014\nunder the 26.8 GB recommended working set of a 32 GB Apple Silicon Mac.\nQuality vs the bf16 weights: PSNR \u2248 35.5 dB on the same seed." | |
| } |