---
license: apache-2.0
tags:
- executorch
- xnnpack
- pte
- on-device
- text-generation
base_model:
- Qwen/Qwen2.5-1.5B
---
# Qwen2.5-1.5B — ExecuTorch XNNPACK 8da4w + 8-bit embedding

`qwen2_5_1_5b_xnnpack_8da4w_e8.pte` (1035.3 MB)

- **Source**: Qwen/Qwen2.5-1.5B — the **base** model, not Instruct. It continues text; it
  does not answer a chat template.
- **License**: Apache-2.0
- **Quantization**: 8da4w linear + 8-bit embedding (`embedding_quantize: "8,0"`)
- **Export**: executorch 1.4.0 `export_llm`, static shape (seq_len=1), max_seq_length 2048,
  XNNPACK extended_ops
- **Config**: `llm_params/qwen2_5_1_5b_xnnpack_8da4w_e8.yaml`

## Verification (Mac arm64, 2026-08-21)

Completions, against the untouched model on the same prompts. A base model is checked this
way because a chat template gets it nowhere: fed one, it echoes the question back.

| prompt | this file | eager fp32 |
|---|---|---|
| "The capital of France is" | turns it into a multiple-choice question and answers A, Paris | " Paris. The capital of France is also the capital of the European Union…" |
| "Water boils at a temperature of" | "100°C and water freezes at a temperature of 0°C" | "212 °F or 100 °C and ice melts at a temperature of 32 °" |
| "Seventeen times four equals" | "what number? To determine what number 17 times 4 equals, we need to perform the multiplication" | "what number? To find the product of 17 and 4, we perform the multiplication: 17 *" |

The continuations differ, which they will once the weights are 4-bit, but nothing here is
wrong where eager is right. Decode **69.5 tok/s**, from one pass with nothing else running.

**The 0.5B of this pair is not on the shelf.** It fails that comparison rather than passing
it: asked where water boils it says 215 degrees Fahrenheit, where eager says 212 °F or
100 °C. Half a billion parameters do not survive 4-bit weights, and XNNPACK has no 8-bit
path that works — `torchao:8da8w` cannot be combined with the delegate and `qmode: int8`
falls over on grouped-query attention. At that size
[LFM2-350M](https://huggingface.co/mlboydaisuke/LFM2-350M-ExecuTorch) is smaller, faster and
correct.

## Converting it

```bash
python convert/export_from_safetensors.py qwen2_5_1_5b
```

Not `export_llm` directly: this repository ships safetensors only, and
`load_checkpoint_from_pytorch_model` reads `pytorch_model.bin`, so the code falls back to
torchtune's checkpointer. Qwen2.5's converter wants one thing from torchtune —
`get_mapped_key` — and ExecuTorch has its own copy of that function, so the script hands the
real one over and stubs the rest.

## Running it

```bash
python llm_params/gen_static.py \
  --pte qwen2_5_1_5b_xnnpack_8da4w_e8.pte \
  --tokenizer tokenizer.json \
  --prompt 'The capital of France is' \
  --eos_ids "[151643]"
```

The 8-bit embedding needs `from executorch.kernels import quantized` before the program is
loaded, and `portable_lib._load_for_executorch` rather than `executorch.runtime`.

(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models) ·
iOS sample: [executorch-samples](https://github.com/john-rocky/executorch-samples))