Instructions to use doggy8088/Qwen3-ASR-1.7B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use doggy8088/Qwen3-ASR-1.7B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-ASR-1.7B-MLX-4bit doggy8088/Qwen3-ASR-1.7B-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
doggy8088/Qwen3-ASR-1.7B-MLX-4bit
This repo contains an MLX conversion of Qwen/Qwen3-ASR-1.7B.
Variant
- Format: MLX safetensors
- Weights dtype: bfloat16
- Quantization: 4-bit
- Custom architecture helper:
qwen3_asr_mlx.py
Files
model*.safetensors: converted MLX weightsconfig.json: converted model configpreprocessor_config.json,tokenizer_config.json,vocab.json,merges.txt: tokenizer / processor assets copied from the source modelqwen3_asr_mlx.py: custom MLX architecture + processor helper used for this conversion
Notes
- Source model:
Qwen/Qwen3-ASR-1.7B - Local conversion directory:
Qwen3-ASR-1.7B-MLX-4bit - This is an Apple Silicon / MLX-oriented conversion.
- For quantized variants, quantizable linear layers are quantized while non-quantizable layers remain in floating point.
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Model size
0.4B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for doggy8088/Qwen3-ASR-1.7B-MLX-4bit
Base model
Qwen/Qwen3-ASR-1.7B