Automatic Speech Recognition
Safetensors
MLX
mlx-audio
qwen3_asr
speech-to-text
speech
transcription
asr
stt
4-bit precision
Instructions to use runfuture/Mega-ASR-MLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use runfuture/Mega-ASR-MLX-Q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Mega-ASR-MLX-Q4 runfuture/Mega-ASR-MLX-Q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
File size: 904 Bytes
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license: apache-2.0
tags:
- mlx
- speech-to-text
- speech
- transcription
- asr
- stt
- mlx-audio
library_name: mlx-audio
pipeline_tag: automatic-speech-recognition
base_model:
- zhifeixie/Mega-ASR
---
# Mega-ASR MLX Q4
This is a private MLX conversion of `zhifeixie/Mega-ASR`.
The checkpoint was produced by merging the `mega-asr-merged` LoRA adapter from
`zhifeixie/Mega-ASR` into the bundled `Qwen3-ASR-1.7B` base checkpoint, then
converting the merged weights to the `mlx-audio` Qwen3-ASR layout.
## Conversion
- Base/source repo: `zhifeixie/Mega-ASR`
- Adapter: `mega-asr-merged`
- Format: MLX / `mlx-audio`
- Quantization: affine Q4, `group_size=64`, `bits=4`
- Text model and token embedding are quantized; audio tower remains full precision.
## Use With mlx-audio
```bash
pip install -U mlx-audio
python -m mlx_audio.stt.generate --model runfuture/Mega-ASR-MLX-Q4 --audio audio.wav
``` |