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README.md

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+---
+library_name: mlx
+license: apache-2.0
+base_model: netflix/void-model
+tags:
+  - mlx
+  - mlx-forge
+  - apple-silicon
+  - safetensors
+  - quantized
+  - int8
+---
+
+# dgrauet/void-model-mlx-q8
+
+Int8 quantization (group_size 64, transformer Linear weights only) of
+[dgrauet/void-model-mlx](https://huggingface.co/dgrauet/void-model-mlx), the MLX
+conversion of [netflix/void-model](https://huggingface.co/netflix/void-model).
+
+Quantized with [mlx-forge](https://github.com/dgrauet/mlx-forge)
+(`mlx-forge convert void-model --quantize --bits 8`).
+
+Good quality/memory balance (~48 GB RAM recommended for the full two-pass
+pipeline). On 32 GB Macs use the q4 variant instead.
+
+## Usage
+
+These weights are loaded by [void-model-mlx](https://github.com/dgrauet/void-model-mlx):
+
+```bash
+python -m void_mlx.infer \
+    --sample sample/BigBen \
+    --pass1 weights/q8/void_pass1.safetensors \
+    --pass2 weights/q8/void_pass2.safetensors \
+    --base-model /path/to/CogVideoX-Fun-V1.5-5b-InP-mlx-q8 \
+    --steps 30 --max-frames 13 --height 352 --width 624 \
+    --output result.gif
+```
+
+Keep `quantize_config.json` next to the weights (the loader also infers
+bits/group_size from the weight shapes if it is missing).
+
+## Related Projects
+
+- **void-model-mlx (inference):** https://github.com/dgrauet/void-model-mlx
+- **VideoX-Fun-mlx (engine):** https://github.com/dgrauet/VideoX-Fun-mlx
+- **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
+- **q4 variant:** https://huggingface.co/dgrauet/void-model-mlx-q4
+
+## Files
+
+- `config.json`
+- `quantize_config.json` (bits=8, group_size=64)
+- `void_pass1.safetensors` (6.68 GB)
+- `void_pass2.safetensors` (6.68 GB)