Instructions to use HanzoHuang/Qwen2.5-3B-Instruct-RKLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use HanzoHuang/Qwen2.5-3B-Instruct-RKLLM with RKLLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Qwen2.5-3B-Instruct-RKLLM
RKLLM-converted Qwen2.5-3B-Instruct language-model artifacts for Rockchip RK3576 and RK3588 NPUs.
These hardware-specific .rkllm files require a compatible Rockchip RKLLM runtime. They are not Transformers checkpoints and cannot be loaded directly with Transformers, llama.cpp, or Ollama.
Base model
- Upstream model: Qwen/Qwen2.5-3B-Instruct
- License: Qwen Research License
- Model type: LLM (text only)
Review the upstream license and usage restrictions before use or redistribution.
Conversion and variants
Toolkit version
RKLLM Toolkit: v1.2.3
Use a file built for the exact target SoC.
| Target | Quantization | File | SHA256 |
|---|---|---|---|
| RK3576 | W4A16 | Qwen2.5-3B-Instruct_RK3576_w4a16.rkllm | 5f2480e10a794848c8d4a5a21a61c015d96570f2a58e4543897b694b59576908 |
| RK3576 | W8A8 | Qwen2.5-3B-Instruct_RK3576_w8a8.rkllm | 5a14ed85f65d3c2890ef8e2b4ab9f094bf9b0992ddebd6f2b9ad8d3b539897b9 |
| RK3588 | W8A8 | Qwen2.5-3B-Instruct_RK3588_w8a8.rkllm | 054a4ac54ea7d483ac17431df5286eb6b2a81d351fbb44b55dc4491f71a7ea46 |
The repository also includes Qwen2.5-3B-Instruct_data_quant.json, used as calibration data during conversion.
Usage
hf download HanzoHuang/Qwen2.5-3B-Instruct-RKLLM \
RK3576/Qwen2.5-3B-Instruct_RK3576_w4a16.rkllm \
--local-dir Qwen2.5-3B-Instruct-RKLLM
Use the Qwen2.5 Instruct chat template with the RKLLM runtime. For Docker deployment, see Hanzo-Huang/rkllm-docker.
Limitations
These are target-specific converted artifacts. Validate quality and runtime compatibility on your Rockchip device.
Acknowledgements
Thanks to the Qwen Team, Rockchip, and the RKLLM community.
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