Instructions to use Alkd/TEA-ASR-1.1-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Alkd/TEA-ASR-1.1-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir TEA-ASR-1.1-MLX-4bit Alkd/TEA-ASR-1.1-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
TEA-ASR-1.1-MLX-4bit
MLX conversion of JacobLinCool/TEA-ASR-1.1 (MIT), a Taiwan-Mandarin ASR model adapted from Qwen/Qwen3-ASR-1.7B that emits Traditional Chinese with Taiwan vocabulary natively and handles Mandarin-English code-switching. All credit for the model goes to the TEA-ASR authors.
Converted with mlx-audio v0.4.5 using mixed affine quantization (the MLX equivalent of the GGUF Q4_K_M + mmproj-Q8_0 recipe).
Quantization recipe
| Component | Quantization |
|---|---|
| Text decoder (+ embeddings) | 4-bit, group size 64, affine |
| Audio tower (AuT encoder + projector) linear layers | 8-bit, group size 64, affine |
| Conv / norm / positional layers | float (MLX has no quantized conv) |
Per-layer bit widths are recorded in config.json under
quantization[<layer path>] (standard MLX per-layer overrides): the audio
tower linear layers carry {"bits": 8, "group_size": 64} entries while the
global default is 4-bit / group size 64.
Usage (Python, mlx-audio)
Note: mlx-audio's stock qwen3_asr loader hardcodes "never quantize the audio tower" and checks that before the per-layer config overrides, so loading this model needs a one-line predicate override:
from mlx_audio.stt.models.qwen3_asr import qwen3_asr
qwen3_asr.Qwen3ASRModel.model_quant_predicate = lambda self, p, m: True
from mlx_audio.stt.utils import load_model
model = load_model("Alkd/TEA-ASR-1.1-MLX-4bit")
result = model.generate("audio.wav", language="Chinese")
print(result.text)
Loaders that follow the per-layer quantization dict in config.json
(mlx-lm-style loading, custom MLX Swift loaders) need no override.
Intended use
Built for the Sumi macOS app (on-device Mandarin voice-to-text) via mlx-audio / mlx-audio-swift.
License
MIT (same as the base checkpoint). The underlying Qwen3-ASR weights remain subject to Apache-2.0 attribution terms.
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