Instructions to use HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-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
Download README.md from HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM: direct link, hf CLI and curl.
- Browser
- Download file 3.19 kB
-
https://huggingface.co/HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM/resolve/main/README.md
- Command line
-
hf download hf://HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM/README.md
-
curl -L -o README.md https://huggingface.co/HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM/resolve/main/README.md
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
Text generation on Rockchip RK3576 and RK3588, converted from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.
- Conversion toolkit: RKLLM Toolkit 1.3.0.
- Inputs / output: text → text.
- License:
mit; the upstream model card describes its terms and intended use.
Choose your files
Use the files for your board’s exact SoC. w4a16 uses 4-bit weights and 16-bit activations; w4a16_g128 adds a weight group size of 128. w8a8 uses 8-bit weights and activations. File size below is download size, not total runtime RAM.
| Board | Quantization | Size | Language model |
|---|---|---|---|
| RK3576 | w4a16_g128 |
1.43 GiB | DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm |
| RK3576 | w8a8 |
1.93 GiB | DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm |
| RK3588 | w8a8 |
1.91 GiB | DeepSeek-R1-Distill-Qwen-1.5B_RK3588_w8a8.rkllm |
Run on your board
With Docker installed on a matching Rockchip board, this example starts the RK3576 w4a16_g128 model and its API on port 8001:
sudo docker run --rm -d --name rkllm \
--privileged -v /dev:/dev -p 8001:8001 \
ghcr.io/hanzo-huang/rkllm-docker/llm/deepseek-r1-distill-qwen-1.5b:rk3576-w4a16-g128
The image includes its model file. See rkllm-docker for API requests, other board variants, and configuration.
To download the files separately instead:
hf download HanzoHuang/DeepSeek-R1-Distill-Qwen-1.5B-RKLLM \
RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm \
--local-dir ./models
These are native RKLLM/RKNN artifacts. Use a compatible RKLLM runtime and the model’s correct chat template when integrating them in another application.
Release notes
No per-variant board benchmark is published here. Quantized output may differ from the original model; check responses on your own workloads. Use the toolkit version listed above when checking runtime compatibility.
SHA-256 checksums
e737c9aa8cfbfd0216e547ed67bc2325d5add2f8b2aa21037389e296ac3b8d80 RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w4a16_g128.rkllm
6ceab2c6b93a8b55837ab75e4050bbce888030153c0bc83a465bd73ead44120f RK3576/DeepSeek-R1-Distill-Qwen-1.5B_RK3576_w8a8.rkllm
d00038d619c1905ac66ab5d499019d80889d37cc1f81be491cc503e284950c2d RK3588/DeepSeek-R1-Distill-Qwen-1.5B_RK3588_w8a8.rkllm
Credits
Original model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. Conversion tools: Rockchip RKLLM.