Automatic Speech Recognition
MLX
Safetensors
moss_transcribe_diarize
int8
speaker-diarization
custom_code
Instructions to use vanch007/mlx-MOSS-Transcribe-Diarize-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vanch007/mlx-MOSS-Transcribe-Diarize-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-MOSS-Transcribe-Diarize-8bit vanch007/mlx-MOSS-Transcribe-Diarize-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-MOSS-Transcribe-Diarize 8bit
8bit MLX weight-only quantized variant of vanch007/mlx-MOSS-Transcribe-Diarize.
Quantization uses MLX affine quantization with bits=8 and group_size=64 on the text backbone, excluding the Whisper audio encoder and VQ adaptor.
python -m moss_transcribe_diarize.mlx.cli /path/to/input.wav \
--model vanch007/mlx-MOSS-Transcribe-Diarize-8bit \
--out-dir runs/mlx_8bit_example
- Downloads last month
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Model size
0.5B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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Quantized
Model tree for vanch007/mlx-MOSS-Transcribe-Diarize-8bit
Base model
OpenMOSS-Team/MOSS-Transcribe-Diarize Finetuned
vanch007/mlx-MOSS-Transcribe-Diarize