Image-Text-to-Text
RKLLM
English
Chinese
rknn
rk3588
rockchip
npu
quantized
vision-language
multimodal
qwen3
Instructions to use GatekeeperZA/Qwen3-VL-4B-Instruct-RKLLM-v1.2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use GatekeeperZA/Qwen3-VL-4B-Instruct-RKLLM-v1.2.3 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
File size: 2,661 Bytes
9bf1a42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | ---
license: apache-2.0
base_model: Qwen/Qwen3-VL-4B-Instruct
tags:
- rkllm
- rknn
- rk3588
- rockchip
- npu
- quantized
- vision-language
- multimodal
- qwen3
language:
- en
- zh
pipeline_tag: image-text-to-text
---
# Qwen3-VL-4B-Instruct — RKLLM v1.2.3 (w8a8, RK3588)
RKLLM/RKNN conversion of [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) for Rockchip RK3588 NPU inference.
Converted with RKLLM Toolkit v1.2.3 (language model) and RKNN Toolkit (vision encoder). This is a multimodal vision-language model — it accepts both images and text as input.
## Key Details
| Property | Value |
|----------|-------|
| Base Model | Qwen/Qwen3-VL-4B-Instruct |
| Toolkit Version | RKLLM Toolkit v1.2.3 / RKNN Toolkit |
| Runtime Version | RKLLM Runtime ≥ v1.2.1 + RKNN Runtime |
| Quantization | w8a8 (8-bit weights, 8-bit activations) |
| Target Platform | RK3588 |
| NPU Cores | 3 |
| Thinking Mode | ❌ Disabled |
| Model Type | Vision-Language (VLM) |
| Languages | English, Chinese (multilingual) |
## Why This Model?
Qwen3-VL-4B-Instruct is Alibaba's 4B vision-language model. It handles image understanding, visual QA, document analysis, and chart reading with strong multilingual support. Running on the RK3588 NPU enables fully local, GPU-free multimodal inference.
Compared to the smaller Qwen3-VL-2B, the 4B variant offers meaningfully better image understanding and text extraction.
## Hardware Tested
- **Orange Pi 5 Plus** — RK3588, 16GB RAM, Armbian Linux
- RKNPU driver 0.9.8
- RKLLM Runtime v1.2.3
## Usage
### With the RKLLM API Server (VLM mode)
```bash
mkdir -p ~/models/qwen3-vl-4b
cd ~/models/qwen3-vl-4b
git lfs install && git clone https://huggingface.co/GatekeeperZA/Qwen3-VL-4B-Instruct-RKLLM-v1.2.3 .
```
Use with [GatekeeperZA/RKLLM-API-Server](https://github.com/GatekeeperZA/RKLLM-API-Server) — the server loads both the `.rkllm` and `.rknn` files automatically when placed in the same directory.
## File Listing
| File | Description |
|------|-------------|
| `qwen3-vl-4b-instruct_w8a8_rk3588.rkllm` | Language model weights for RK3588 NPU |
| `qwen3-vl-4b-vision_rk3588.rknn` | Vision encoder for RK3588 NPU |
## Compatibility Notes
- Minimum runtime: RKLLM Runtime v1.2.1 + RKNN Runtime v2.x. v1.2.3 recommended.
- RKNPU driver: ≥ 0.9.6
- SoCs: RK3588 / RK3588S. Not compatible with RK3576 without reconversion.
- RAM: ~5.5GB loaded. Requires 8GB+ board (16GB recommended).
## Acknowledgements
- Alibaba Qwen Team for Qwen3-VL
- Rockchip / airockchip for the RKLLM and RKNN toolkits
- Converted by [GatekeeperZA](https://huggingface.co/GatekeeperZA)
|