File size: 2,300 Bytes
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license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
library_name: rkllm
tags:
- rkllm
- rockchip
- rk3576
- rk3588
- deepseek
- deepseek-r1
- qwen
---
# DeepSeek-R1-Distill-Qwen-1.5B-RKLLM
RKLLM-converted DeepSeek-R1-Distill-Qwen-1.5B 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: [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
- License: MIT
- Model type: LLM (text only)
## Conversion and variants
### Toolkit version
**RKLLM Toolkit: v1.3.0**
Use a file built for the exact target SoC.
| Target | Quantization | File | SHA256 |
| --- | --- | --- | --- |
| RK3576 | W4A16-g128 | [DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm](RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm) | `e737c9aa8cfbfd0216e547ed67bc2325d5add2f8b2aa21037389e296ac3b8d80` |
| RK3576 | W8A8 | [DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm](RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm) | `6ceab2c6b93a8b55837ab75e4050bbce888030153c0bc83a465bd73ead44120f` |
| RK3588 | W8A8 | [DeepSeek-R1-Distill-Qwen-1.5B_RK3588_w8a8.rkllm](RK3588/DeepSeek-R1-Distill-Qwen-1.5B_RK3588_w8a8.rkllm) | `d00038d619c1905ac66ab5d499019d80889d37cc1f81be491cc503e284950c2d` |
The repository also includes `DeepSeek-R1-Distill-Qwen-1.5B_data_quant.json`, used as calibration data during conversion.
## Usage
```bash
hf download HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM \
RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm \
--local-dir DeepSeek-R1-Distill-Qwen-1.5B-RKLLM
```
Use the upstream DeepSeek R1 chat template with the RKLLM runtime. For Docker deployment, see [Hanzo-Huang/rkllm-docker](https://github.com/Hanzo-Huang/rkllm-docker).
## Limitations
These are target-specific converted artifacts. Quantization and conversion can change output quality relative to the upstream model; validate the result on your Rockchip device.
## Acknowledgements
Thanks to DeepSeek, the Qwen Team, Rockchip, and the RKLLM community.
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