Complete RKLLM model card

README.md

@@ -1,3 +1,60 @@
 ---
 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: v1.2.3**
+
+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.