Instructions to use mlx-community/Ming-3.0-asr-tiny-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Ming-3.0-asr-tiny-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/Ming-3.0-asr-tiny-bf16 --local-dir Ming-3.0-asr-tiny-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Ming-3.0 ASR Tiny for MLX
Converted from inclusionAI/Ming-3.0-asr-tiny.
English and Chinese transcription. Input audio is resampled to 16 kHz. The audio encoder and MoE routers retain floating-point precision. Speculative MTP decoding and timestamps are not implemented.
from mlx_audio.stt import load
model = load('mlx-community/Ming-3.0-asr-tiny-bf16', strict=True)
result = model.generate("speech.wav")
print(result.text)
Offline inference on Apple Silicon using native MLX code. The tokenizer is loaded without executing remote code.
Source checkpoint revision: b610298956f4d190fa517d396d7e0337a6bf039f.
This export includes all weights and tokenizers needed for its supported tasks. No separate model downloads or ONNX packages are required. Use an MLX Audio version with Ming Tiny support.
The example repository name above may be replaced with the local export directory or your uploaded repository ID.
Validation: strictly reloaded from a relocated directory with an empty Hugging Face cache, network access blocked and ONNX packages absent. See validation.json for the tested tasks and weight inventory. SHA256SUMS covers all package files.
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Model tree for mlx-community/Ming-3.0-asr-tiny-bf16
Base model
inclusionAI/Ming-3.0-asr-tiny