license: apache-2.0
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: rkllm
tags:
- rkllm
- rockchip
- rk3576
- rk3588
- qwen
- qwen3
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🚀 Deploy with reComputer AI Lab
Explore and deploy AI models on Rockchip RK3576 and RK3588 devices with reComputer AI Lab.
⚡ 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.
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Qwen3-1.7B · RKLLM
- Max context length: 4096
Text generation on Rockchip RK3576 and RK3588, converted from Qwen/Qwen3-1.7B.
- Conversion toolkit: RKLLM Toolkit 1.3.0.
- Inputs / output: text → text.
- License:
apache-2.0; 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 |
1.57 GiB | Qwen3-1.7B_RK3576_w4a16.rkllm |
| RK3576 | w4a16_g128 |
1.69 GiB | Qwen3-1.7B_RK3576_w4a16_g128.rkllm |
| RK3576 | w8a8 |
2.22 GiB | Qwen3-1.7B_RK3576_w8a8.rkllm |
| RK3588 | w8a8 |
2.21 GiB | 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:
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 for API requests, other board variants, and configuration.
To download the files separately instead:
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.
SHA-256 checksums
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
Credits
Original model: Qwen/Qwen3-1.7B. Conversion tools: Rockchip RKLLM.