Instructions to use mutableworlds/Voxtral-4B-TTS-2603-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mutableworlds/Voxtral-4B-TTS-2603-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mutableworlds/Voxtral-4B-TTS-2603-MLX-4bit --local-dir Voxtral-4B-TTS-2603-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Voxtral 4B TTS, MLX 4-bit Quantized
4-bit quantized weights for Voxtral 4B TTS on Apple Silicon via MLX.
Backbone: 4-bit (group_size=64), ~2.6 GB (down from ~6.8 GB BF16) Acoustic transformer: BF16 (unchanged) Vocoder: BF16, pre-processed (weight-norm reconstructed, conv weights transposed, codebook precomputed)
Total file size: 3.4 GB (vs 7.5 GB BF16)
Usage
Requires the code from redseaplume/Voxtral-4B-TTS-2603-MLX. Point model_path at this repo.
What's in here
consolidated.safetensors: all three components in one fileparams.json: model configtekken.json: tokenizervoice_embedding/: 20 pre-computed voice embeddings (.pt and .npz)
Notes
- Only the backbone is quantized. Acoustic transformer and vocoder stay BF16.
- Generation output differs slightly from BF16 (quantization is lossy). Frame counts may vary.
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Hardware compatibility
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Quantized
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Model tree for mutableworlds/Voxtral-4B-TTS-2603-MLX-4bit
Base model
mistralai/Ministral-3-3B-Base-2512 Finetuned
mistralai/Voxtral-4B-TTS-2603