Instructions to use xocialize/nacre-v1-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use xocialize/nacre-v1-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download xocialize/nacre-v1-mlx --local-dir nacre-v1-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Nacre v1 โ MLX layout
The Nacre v1 weights converted for the Swift/MLX port
(mlx-nacre-swift): conv weights OHWI, per-tensor exact from the release (oracle/convert_weights.py). Two
tensors of the release are not parameters of the MLX module tree and are omitted: the Swin
relative_position_index buffers (rebuilt from the window size) and the VQ codebook (Nacre decodes continuous
latents).
| file | lane |
|---|---|
nacre_v1_mlx_fp16.safetensors + vq_f4_mlx_fp16.safetensors |
shipping (every reduction is fp32 inside the port) |
nacre_v1_mlx_fp32.safetensors + vq_f4_mlx_fp32.safetensors |
parity |
Licences, provenance, metrics and limitations: see the main model card. The VQ-f4 autoencoder is CompVis (MIT); see NOTICE.
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