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
File size: 1,433 Bytes
118dcc3 559a94b 118dcc3 559a94b 118dcc3 559a94b 118dcc3 559a94b 29ee4d4 118dcc3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"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."
} |