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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-ASR-1.7B
+tags:
+  - coreml
+  - asr
+  - speech-to-text
+  - qwen3
+  - on-device
+  - apple
+  - macos
+language:
+  - zh
+  - en
+  - ja
+  - ko
+  - multilingual
+pipeline_tag: automatic-speech-recognition
+library_name: coremltools
+---
+
+# Qwen3-ASR-1.7B CoreML
+
+CoreML conversion of [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) for on-device speech-to-text on macOS/iOS.
+
+Optimized with **FP32 compute + ANEMLL RMSNorm + mixed-precision INT8** to prevent decoder overflow while keeping model size small.
+
+## Files
+
+| File | Description | Size |
+|------|-------------|------|
+| `qwen3_asr_encoder_int8.mlpackage/` | Audio encoder (Conv2D + 24-layer Transformer + projection). FP16 compute, INT8 per-channel weights. | 304 MB |
+| `qwen3_asr_decoder_f32_anemll_int8-mixed.mlpackage/` | Decoder (28-layer causal LLM + LM head). FP32 compute, ANEMLL RMSNorm, mixed-precision INT8. | 2.8 GB |
+| `qwen3_asr_embeddings.bin` | Token embedding table (FP16, shape 151936 x 2048). Loaded separately to avoid quantizing embeddings. | 594 MB |
+
+## Architecture
+
+```
+Audio (16kHz) → Mel Spectrogram (128 bins)
+  → Encoder (100-frame chunks → 13 tokens each)
+  → Embedding lookup + prompt construction
+  → Decoder (autoregressive, RoPE as inputs, KV cache)
+  → Token IDs → Text
+```
+
+### Encoder
+
+- 3x Conv2D (stride 2) + 24-layer Transformer + 2-layer projection
+- Input: `[1, 1, 128, 100]` mel chunk → Output: `[1, 13, 2048]` audio features
+- Bidirectional attention, sinusoidal positional embeddings (reset per chunk)
+- INT8 per-channel quantization (first conv layer kept at FP16)
+
+### Decoder
+
+- 28-layer causal Transformer with GQA (16 Q heads, 8 KV heads, head_dim=128)
+- SwiGLU MLP (intermediate_size=11008)
+- RoPE theta=1,000,000 (NeoX/split-half style, passed as cos/sin inputs)
+- ANEMLL-style RMSNorm: `LayerNorm([x, -x])` trick for numerical stability
+- Mixed-precision INT8: first/last layer, norms, LM head kept at FP16
+
+### Key Quality Improvements
+
+1. **FP32 compute for decoder** — Hidden states reach 10,876 at layer 25; x^2 = 118M >> FP16 max 65,504. FP32 is mandatory.
+2. **ANEMLL RMSNorm** — Uses native `layer_norm` op via `[x, -x]` concatenation for GPU/ANE precision.
+3. **Mixed-precision INT8** — Skips 72 sensitive ops (embeddings, first/last layer, all norms, LM head).
+4. **RoPE as inputs** — Precomputed sin/cos passed in to avoid in-model precision loss.
+5. **Embeddings extracted** — 151,936-token embedding table stored as raw FP16 binary, not quantized.
+
+## Quality Benchmarks
+
+### English (JFK speech, 11s)
+
+| Pipeline | Transcription | Match |
+|----------|---------------|-------|
+| PyTorch FP32 | "And so, my fellow Americans, ask not what your country can do for you. Ask what you can do for your country." | Reference |
+| CoreML INT8 | Same text (minor punctuation difference: "." vs ";") | **MATCH** |
+
+### Taiwan Mandarin (Taiwan-Tongues test set, 20 samples, 60s total)
+
+| Metric | Value |
+|--------|-------|
+| PyTorch vs CoreML match rate | **19/20 (95%)** |
+| Average CER (PyTorch) | 0.2009 |
+| Average CER (CoreML INT8) | 0.2064 |
+| CER difference | **+0.0056** |
+
+## Usage Notes
+
+- **macOS 14+** required (minimum deployment target)
+- Decoder runs on **GPU only** (FP32 compute prevents ANE execution)
+- KV cache must be initialized with **1 dummy slot** (CoreML crashes on size-0 dynamic dims); mask the dummy position with `-inf`
+- Encoder processes mel in **100-frame chunks** — split mel, run encoder per chunk, concatenate outputs
+
+## Conversion Details
+
+Converted using `coremltools 9.0` with PyTorch 2.10.0. Conversion scripts available at the source repository.
+
+```
+compute_precision: FLOAT32 (decoder), FLOAT16 (encoder)
+minimum_deployment_target: macOS14
+quantization: linear_symmetric INT8 per-channel (mixed-precision for decoder)
+```
+
+## License
+
+Apache 2.0 (same as base model)