Instructions to use littoralai/cohere-transcribe-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use littoralai/cohere-transcribe-mlx-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir cohere-transcribe-mlx-8bit littoralai/cohere-transcribe-mlx-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
littoralai/cohere-transcribe-mlx-8bit
8-bit MLX quantization of CohereLabs/cohere-transcribe-03-2026, via the MLX fp16 conversion at littoralai/cohere-transcribe-mlx-fp16. Quantized with mlx-audio (8-bit, group size 64, affine). No architecture, vocabulary, or training changes. See the base model card for capabilities, training details, and evaluations.
License
Apache-2.0, from the base model. The custom architecture code
(modeling_cohere_asr.py, configuration_cohere_asr.py,
processing_cohere_asr.py, tokenization_cohere_asr.py) is Cohere's,
redistributed unchanged. Changes vs. the source: fp16 MLX conversion, then
8-bit affine quantization.
Integrity
model.safetensors sha256
aadaf8d3388975853385400c9bd8dee92a71e12a83860f35bebaac345aa8af93
Use with mlx-audio
python -m mlx_audio.stt.generate --model littoralai/cohere-transcribe-mlx-8bit \
--audio audio.wav --output out
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8-bit
Model tree for littoralai/cohere-transcribe-mlx-8bit
Base model
CohereLabs/cohere-transcribe-03-2026