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---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: rkllm
tags:
- rkllm
- rockchip
- rk3576
- rk3588
- qwen
- qwen3
---
**English** | [简体中文](README_zh-CN.md)

## 🚀 Deploy with reComputer AI Lab

Explore and deploy AI models on Rockchip **RK3576** and **RK3588** devices with **reComputer AI Lab**.

[![reComputer AI Lab — explore models and deployment guides](https://files.seeedstudio.com/Edge_box/reComputer_Lab.webp)](https://sensecraft.seeed.cc/ai-lab/en/models?device=rk3588)

### ⚡ Run with one command

With Docker installed, choose your model and board in reComputer AI Lab, copy the deployment command, and run it in your device’s terminal.

**[Explore Models & Deployment Guides →](https://sensecraft.seeed.cc/ai-lab/en/models?device=rk3588)**

---


# Qwen3-1.7B · RKLLM

* Max context length: 4096

Text generation on Rockchip RK3576 and RK3588, converted from [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).

- **Conversion toolkit:** RKLLM Toolkit 1.3.0.
- **Inputs / output:** text → text.
- **License:** `apache-2.0`; the [upstream model card](https://huggingface.co/Qwen/Qwen3-1.7B) 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` | 1.57 GiB | [Qwen3-1.7B_RK3576_w4a16.rkllm](RK3576/Qwen3-1.7B_RK3576_w4a16.rkllm) |
| RK3576 | `w4a16_g128` | 1.69 GiB | [Qwen3-1.7B_RK3576_w4a16_g128.rkllm](RK3576/Qwen3-1.7B_RK3576_w4a16_g128.rkllm) |
| RK3576 | `w8a8` | 2.22 GiB | [Qwen3-1.7B_RK3576_w8a8.rkllm](RK3576/Qwen3-1.7B_RK3576_w8a8.rkllm) |
| RK3588 | `w8a8` | 2.21 GiB | [Qwen3-1.7B_RK3588_w8a8.rkllm](RK3588/Qwen3-1.7B_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/qwen3-1.7b: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/Qwen3-1.7B-RKLLM \
  RK3576/Qwen3-1.7B_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
9f5f2facdef5f1da27756524f0284b307d5e4cee45ae9d9c8a02b6e9d8888ce2  RK3576/Qwen3-1.7B_RK3576_w4a16.rkllm
c72954ad5bd155aa49f89a86cb2133b79fafd502685d345973e26bb07a9c14a7  RK3576/Qwen3-1.7B_RK3576_w4a16_g128.rkllm
2d1c1ec4e50a9edb85b801f546d935943481e7671d3a73d19a2df9f18d4da2ca  RK3576/Qwen3-1.7B_RK3576_w8a8.rkllm
384294b54bf68b61590c4d818c33e27d67dbeced51fb9f1f93014f0ad06d2545  RK3588/Qwen3-1.7B_RK3588_w8a8.rkllm
```
</details>
## Credits
Original model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). Conversion tools: [Rockchip RKLLM](https://github.com/airockchip/rknn-llm).

	---
	license: apache-2.0
	base_model: Qwen/Qwen3-1.7B
	pipeline_tag: text-generation
	library_name: rkllm
	tags:
	- rkllm
	- rockchip
	- rk3576
	- rk3588
	- qwen
	- qwen3
	---

	English \| [简体中文](README_zh-CN.md)

	## 🚀 Deploy with reComputer AI Lab

	Explore and deploy AI models on Rockchip RK3576 and RK3588 devices with reComputer AI Lab.

	[![reComputer AI Lab — explore models and deployment guides](https://files.seeedstudio.com/Edge_box/reComputer_Lab.webp)](https://sensecraft.seeed.cc/ai-lab/en/models?device=rk3588)

	### ⚡ Run with one command

	With Docker installed, choose your model and board in reComputer AI Lab, copy the deployment command, and run it in your device’s terminal.

	[Explore Models & Deployment Guides →](https://sensecraft.seeed.cc/ai-lab/en/models?device=rk3588)

	---


	# Qwen3-1.7B · RKLLM

	* Max context length: 4096

	Text generation on Rockchip RK3576 and RK3588, converted from [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).

	- Conversion toolkit: RKLLM Toolkit 1.3.0.
	- Inputs / output: text → text.
	- License: `apache-2.0`; the [upstream model card](https://huggingface.co/Qwen/Qwen3-1.7B) 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` \| 1.57 GiB \| [Qwen3-1.7B_RK3576_w4a16.rkllm](RK3576/Qwen3-1.7B_RK3576_w4a16.rkllm) \|
	\| RK3576 \| `w4a16_g128` \| 1.69 GiB \| [Qwen3-1.7B_RK3576_w4a16_g128.rkllm](RK3576/Qwen3-1.7B_RK3576_w4a16_g128.rkllm) \|
	\| RK3576 \| `w8a8` \| 2.22 GiB \| [Qwen3-1.7B_RK3576_w8a8.rkllm](RK3576/Qwen3-1.7B_RK3576_w8a8.rkllm) \|
	\| RK3588 \| `w8a8` \| 2.21 GiB \| [Qwen3-1.7B_RK3588_w8a8.rkllm](RK3588/Qwen3-1.7B_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/qwen3-1.7b: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/Qwen3-1.7B-RKLLM \
	RK3576/Qwen3-1.7B_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
	9f5f2facdef5f1da27756524f0284b307d5e4cee45ae9d9c8a02b6e9d8888ce2 RK3576/Qwen3-1.7B_RK3576_w4a16.rkllm
	c72954ad5bd155aa49f89a86cb2133b79fafd502685d345973e26bb07a9c14a7 RK3576/Qwen3-1.7B_RK3576_w4a16_g128.rkllm
	2d1c1ec4e50a9edb85b801f546d935943481e7671d3a73d19a2df9f18d4da2ca RK3576/Qwen3-1.7B_RK3576_w8a8.rkllm
	384294b54bf68b61590c4d818c33e27d67dbeced51fb9f1f93014f0ad06d2545 RK3588/Qwen3-1.7B_RK3588_w8a8.rkllm
	```

	</details>

	## Credits

	Original model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). Conversion tools: [Rockchip RKLLM](https://github.com/airockchip/rknn-llm).