Instructions to use jasonvassallo/mlx-musicgen-stereo-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jasonvassallo/mlx-musicgen-stereo-small with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-musicgen-stereo-small jasonvassallo/mlx-musicgen-stereo-small
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-musicgen-stereo-small (MusicGen Stereo Small, MLX)
Non-commercial licence. These weights are CC-BY-NC-4.0 and may not be used for commercial purposes. See Licence below.
MLX conversion of facebook/musicgen-stereo-small for inference on Apple silicon via mlx-audiogen.
Licence
Upstream MusicGen is released under two different licences, and it matters which applies to what:
- Model weights — CC-BY-NC-4.0 (non-commercial). That is what this repository contains, so that is what governs it.
- The upstream code is MIT. That does not extend to these weights.
Key terms, summarised (the licence text governs):
- Free to share and adapt for non-commercial purposes.
- You must give appropriate credit and indicate if changes were made.
- No commercial use — including use primarily directed towards commercial advantage or monetary compensation.
mlx-audiogen's own Apache-2.0 licence covers the conversion code only. The full licence is in LICENSE; attribution and the statement of modifications are in NOTICE.
Attribution
MusicGen Stereo Small was created by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez, and published in Simple and Controllable Music Generation.
Modifications
These weights are a modified version of the upstream checkpoint, converted to the MLX array format for inference on Apple silicon. Specifically: convolution weights are transposed where MLX's layout differs from PyTorch's, tensors are re-serialised as safetensors, weight keys are remapped to match this project's module names, and the text encoder is split into a separate file. The model is re-implemented against MLX rather than PyTorch.
No weights are retrained or fine-tuned.
Tensor precision is unchanged: the upstream checkpoint already stores float16 and so does this conversion. No weight value is altered by rounding — the float16 storage is upstream's choice, not a reduction introduced here.
Bundled components
- T5 text encoder — https://huggingface.co/google-t5/t5-base, Apache-2.0, Google LLC. Licence text:
LICENSE.t5-apache-2.0
Where component licences differ, the most restrictive term governs this repository: CC-BY-NC-4.0.
Usage
Generation via
mlx-audiogenis not supported for the stereo variants yet.MusicGen stereo uses 8 codebooks, but the pipeline loads the shared mono 32 kHz EnCodec (a 4-quantizer model) for every variant, so decoding raises
IndexError. These weights are published for conversion parity and for use once stereo decoding lands; the mono variants work today.
pip install "mlx-audiogen[server]"
mlx-audiogen --model musicgen --prompt "happy rock song" --seconds 5 \
--weights-dir musicgen-small # a mono variant
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Quantized
Model tree for jasonvassallo/mlx-musicgen-stereo-small
Base model
facebook/musicgen-stereo-small