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README.md
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---
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license: apache-2.0
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tags:
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- executorch
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- xnnpack
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- pte
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- on-device
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- text-generation
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base_model:
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- Qwen/Qwen3-4B
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---
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# Qwen3-4B — ExecuTorch XNNPACK 8da4w + 8-bit embedding
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`qwen3_4b_xnnpack_8da4w_e8.pte` (2469.4 MB)
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- **Source**: Qwen/Qwen3-4B
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- **License**: Apache-2.0
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- **Quantization**: 8da4w linear + 8-bit embedding (`embedding_quantize: "8,0"`)
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- **Export**: executorch 1.4.0 `export_llm`, static shape (seq_len=1), max_seq_length 2048,
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XNNPACK extended_ops
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- **Config**: `llm_params/qwen3_4b_xnnpack_8da4w_e8.yaml`
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## Verification (Mac arm64, 2026-08-21)
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`llm_params/gen_static.py`, token-by-token prefill then greedy decode, a fresh process per
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prompt so no answer is read through the previous one's cache:
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| prompt | answer |
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|---|---|
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| capital of France? | opens a `<think>` block and reasons before answering |
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| 17 times 4? | same, working the multiplication out in the block |
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Decode **28.9 tok/s**, from one pass over every model on this shelf with nothing else
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running. That matters more than it sounds: the same file measured a quarter of its rate
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while an export was running alongside.
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Chat template: ChatML, bos 151643, eos [151645, 151643].
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Not measured on a phone.
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## Three things checked before exporting
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- **`use_sdpa_with_kv_cache` is on.** Upstream's `qwen3_5` config leaves it off with no
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reason given while the equally hybrid `lfm2` config has it on; measured on Qwen3.5-2B in
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one run, that is 8.20 tok/s against 16.64.
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- **`dim` and `hidden_dim` both divide by the quantizer's group size.** 8da4w only touches a
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linear whose in_features divide by it, and skips the rest silently — SmolLM2-135M, which
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is 576 wide, came out at 475 MB against fp32's 540 with no warning at all.
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- **Every field of the params json is read by the generic path**, via
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`convert/check_params_used.py`. SmolLM3 sets `no_rope_layer_interval`, which `ModelArgs`
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declares and only the MLX and Qualcomm backends read, and it exports fine and then repeats
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a single word forever.
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## Running it
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```bash
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python llm_params/gen_static.py \
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--pte qwen3_4b_xnnpack_8da4w_e8.pte \
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--tokenizer tokenizer.json \
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--prompt $'<|im_start|>user\nWhat is the capital of France?<|im_end|>\n<|im_start|>assistant\n' \
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--eos_ids "[151645, 151643]"
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```
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The 8-bit embedding needs `from executorch.kernels import quantized` before the program is
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loaded, and `portable_lib._load_for_executorch` rather than `executorch.runtime`. Without
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that the method will not load at all — `kernel
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'quantized_decomposed::embedding_byte.dtype_out' not found` — which reads like a broken
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export rather than a runtime missing its kernels.
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(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models) ·
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iOS sample: [executorch-samples](https://github.com/john-rocky/executorch-samples))
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