Automatic Speech Recognition
MLX
Safetensors
voxtral_realtime
voxtral
audio
speech
speech-recognition
realtime
streaming
asr
turboquant
quantization
4-bit precision
Instructions to use majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-4bit majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Card accuracy pass 2: remove unmeasured speed claims, honest brand labels
Browse files
README.md
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- [`majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit)
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- [`majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-2bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-2bit)
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- [`majentik/Voxtral-Mini-4B-Realtime-2602-RotorQuant-MLX-4bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-RotorQuant-MLX-4bit)
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- [`mistralai/Voxtral-Mini-4B-Realtime-2602`](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) — upstream base model
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- [`majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit)
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- [`majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-2bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-2bit)
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- [`majentik/Voxtral-Mini-4B-Realtime-2602-RotorQuant-MLX-4bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-RotorQuant-MLX-4bit)
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- [`mistralai/Voxtral-Mini-4B-Realtime-2602`](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) — upstream base model
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## About the RotorQuant / TurboQuant labels
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RotorQuant and TurboQuant are this project's **release labels**, not distinct
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quantization algorithms — for any given tier, both brand repos carry
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byte-identical weights produced with the standard MLX / llama.cpp quantizers.
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No brand-specific speedup is claimed or measured.
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