---
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
pipeline_tag: text-generation
library_name: rkllm
tags:
- rkllm
- rockchip
- rk3576
- rk3588
- qwen
- qwen2
---

# Qwen2-0.5B-Instruct · RKLLM

Text generation on Rockchip RK3576 and RK3588, converted from [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).

- **Conversion toolkit:** RKLLM Toolkit 1.3.0.
- **Inputs / output:** text → text.
- **License:** `apache-2.0`; the [upstream model card](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) 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` | 0.64 GiB | [Qwen2-0.5B-Instruct_RK3576_w4a16_g128.rkllm](RK3576/Qwen2-0.5B-Instruct_RK3576_w4a16_g128.rkllm) |
| RK3576 | `w8a8` | 0.76 GiB | [Qwen2-0.5B-Instruct_RK3576_w8a8.rkllm](RK3576/Qwen2-0.5B-Instruct_RK3576_w8a8.rkllm) |
| RK3588 | `w8a8` | 0.75 GiB | [Qwen2-0.5B-Instruct_RK3588_w8a8.rkllm](RK3588/Qwen2-0.5B-Instruct_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:

```bash
sudo docker run --rm -d --name rkllm \
  --privileged -v /dev:/dev -p 8001:8001 \
  ghcr.io/hanzo-huang/rkllm-docker/llm/qwen2-0.5b-instruct:rk3576-w4a16-g128
```

The image includes its model file. See [rkllm-docker](https://github.com/Hanzo-Huang/rkllm-docker#readme) for API requests, other board variants, and configuration.

To download the files separately instead:

```bash
hf download HanzoHuang/Qwen2-0.5B-Instruct-RKLLM \
  RK3576/Qwen2-0.5B-Instruct_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.

<details>
<summary>SHA-256 checksums</summary>

```text
a166cdcff5d8a2e33423d01d8bac6f7a6ad1e1130fbd0bfefdb8b69082113e87  RK3576/Qwen2-0.5B-Instruct_RK3576_w4a16_g128.rkllm
cdccb51e23131352823af8becf9ac4acb15cb30f0f961e89d16cb0fabd003414  RK3576/Qwen2-0.5B-Instruct_RK3576_w8a8.rkllm
24e99eeea1512d6e7f3f120f3344a510933301dfcb72bd64b24a9724ab48e1d8  RK3588/Qwen2-0.5B-Instruct_RK3588_w8a8.rkllm
```

</details>

## Credits

Original model: [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct). Conversion tools: [Rockchip RKLLM](https://github.com/airockchip/rknn-llm).