Qwen3-4B-Instruct-2507 LiteRT-LM Model

This repository contains LiteRT-LM variants of Qwen/Qwen3-4B-Instruct-2507 optimized for on-device text generation.

Available Artifacts

File	Quantization	Context	Size
qwen3_4b_instruct_2507_mixed_int4.litertlm	TorchAO mixed INT4, float KV	2048	2535.88 MiB

Conversion Notes

The mixed INT4 .litertlm artifact was produced with a TorchAO-based quantize-first recipe from the original Hugging Face checkpoint. This is a mixed quantization layout rather than a uniform all-INT4 model: eligible linear projection weights are stored as blockwise INT4 with group size 32 and floating-point scales, token embedding weights use weight-only INT8 quantization, and normalization/reduction paths plus KV cache tensors remain floating point.

The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including odml.runtime_bmm and odml.cache_update.

Performance

Desktop benchmark: AMD Radeon AI PRO R9700, LiteRT-LM WebGPU, 256 prefill tokens, 32 decode tokens. Android rows use LiteRT-LM v0.13.1 with GPU OpenCL, 256 prefill tokens, and 64 decode tokens. Values report the warmed iteration from a two-iteration run unless noted.

Hardware benchmark disclosure: Results were measured by us on retail devices purchased through normal channels. These results are not affiliated with, sponsored by, endorsed by, or verified by Samsung, vivo, Qualcomm, MediaTek, Google, MLCommons, or Hugging Face. Results depend on device SKU, OS build, thermal state, battery mode, backend, model quantization, runtime version, and benchmark settings.

Device / Backend	Prefill (tok/s)	Decode (tok/s)	TTFT (s)	Peak Private Footprint
Desktop GPU WebGPU	1324.75	100.75	0.20	1697 MB
Samsung SM-S937U1 GPU OpenCL	375.67	18.51	0.74	1587 MB
vivo V2502A GPU OpenCL	174.12	12.60	1.55	4720 MB
TECNO LJ9 GPU OpenCL	102.57	9.65	2.60	4903 MB

Try It

Install uv and run:

uv tool install litert-lm
uvx litert-lm run --from-huggingface-repo=litert-community/Qwen3-4B-Instruct-2507 qwen3_4b_instruct_2507_mixed_int4.litertlm --prompt="What is the capital of France?"

Integration

Ready to integrate this into your product? Get started in the LiteRT-LM documentation.

Citation

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report},
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388},
}