Instructions to use ryancod/Qwen3-ASR-1.7B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ryancod/Qwen3-ASR-1.7B-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-ASR-1.7B-MLX-8bit ryancod/Qwen3-ASR-1.7B-MLX-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Qwen3-ASR-1.7B MLX 8-bit (pinned mirror)
Byte-identical mirror of aufklarer/Qwen3-ASR-1.7B-MLX-8bit (an 8-bit MLX
quantization of Qwen/Qwen3-ASR-1.7B, Apache-2.0), frozen at the revision
validated against the Qwen3-ASR technical report's benchmarks:
LibriSpeech test-clean WER 1.66%; FLEURS es 3.1 / de 3.8 / fr 5.0 WER,
zh 2.34 / ko 1.99 / ja 5.86 CER (paper-style scoring).
Mirrored so a consuming app can pin provenance and verify SHA-256 checksums rather than tracking a third-party branch tip.
SHA-256:
1b76b3b6c655fc54595da025f7a96474ad9fa86363303fbdd61a7d8483ccfaf7 config.json
8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5 merges.txt
bf304b009cc7eca79283056f787b44c952d24ac22cec787b39732bba3c23c13c model.safetensors
0a5d0ec11188602242ff81a9969883d0fdeb98cd5d85cd1413089d897c201af5 model.safetensors.index.json
4942d005604266809309cabc9f4e9cb89ce855d59b14681fdc0e1cc62ea26c4c tokenizer_config.json
ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910 vocab.json
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Model size
0.8B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
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Qwen/Qwen3-ASR-1.7B