Instructions to use HanzoHuang/Qwen3-VL-4B-Instruct-RKLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use HanzoHuang/Qwen3-VL-4B-Instruct-RKLLM 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
Release validated RKLLM 1.3.1 VLM artifacts
Browse files- .gitattributes +5 -0
- LICENSE +202 -0
- README.md +98 -0
- SHA256SUMS +5 -0
- v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm +3 -0
- v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w8a8.rkllm +3 -0
- v1.3.1/RK3576/Qwen3-VL-4B-Instruct_vision_RK3576.rknn +3 -0
- v1.3.1/RK3588/Qwen3-VL-4B-Instruct_RK3588_w8a8.rkllm +3 -0
- v1.3.1/RK3588/Qwen3-VL-4B-Instruct_vision_RK3588.rknn +3 -0
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README.md
ADDED
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
base_model: Qwen/Qwen3-VL-4B-Instruct
|
| 4 |
+
library_name: rkllm
|
| 5 |
+
pipeline_tag: image-text-to-text
|
| 6 |
+
tags: [rkllm, rockchip, rk3576, rk3588, qwen3-vl]
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Qwen3-VL-4B-Instruct · RKLLM
|
| 10 |
+
|
| 11 |
+
Text and image chat on Rockchip RK3576 and RK3588, converted from [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct).
|
| 12 |
+
|
| 13 |
+
* Max context length: 8192
|
| 14 |
+
|
| 15 |
+
- **Conversion toolkit:** RKLLM Toolkit 1.3.1 and RKNN Toolkit2 2.3.2.
|
| 16 |
+
- **Inputs / output:** text and one image → text. Video and audio input are unavailable in this runtime.
|
| 17 |
+
- **License:** Apache 2.0; see the [original model](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) and [license](LICENSE).
|
| 18 |
+
|
| 19 |
+
## Choose your files
|
| 20 |
+
|
| 21 |
+
Use files for your board's exact SoC. RK3576 offers compact W4A16-g128 and W8A8; RK3588 uses W8A8. Download size is not runtime RAM.
|
| 22 |
+
|
| 23 |
+
| Board | Quantization | Size | Language model |
|
| 24 |
+
| --- | --- | --- | --- |
|
| 25 |
+
| RK3576 | W4A16-g128 | 3.16 GiB | [Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm](v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm) |
|
| 26 |
+
| RK3576 | w8a8 | 4.62 GiB | [Qwen3-VL-4B-Instruct_RK3576_w8a8.rkllm](v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w8a8.rkllm) |
|
| 27 |
+
| RK3588 | w8a8 | 4.52 GiB | [Qwen3-VL-4B-Instruct_RK3588_w8a8.rkllm](v1.3.1/RK3588/Qwen3-VL-4B-Instruct_RK3588_w8a8.rkllm) |
|
| 28 |
+
|
| 29 |
+
Every language model requires its board's matching vision encoder below. Both RK3576 language variants share the RK3576 encoder.
|
| 30 |
+
|
| 31 |
+
| Board | Size | Vision encoder |
|
| 32 |
+
| --- | --- | --- |
|
| 33 |
+
| RK3576 | 0.83 GiB | [Qwen3-VL-4B-Instruct_vision_RK3576.rknn](v1.3.1/RK3576/Qwen3-VL-4B-Instruct_vision_RK3576.rknn) |
|
| 34 |
+
| RK3588 | 0.81 GiB | [Qwen3-VL-4B-Instruct_vision_RK3588.rknn](v1.3.1/RK3588/Qwen3-VL-4B-Instruct_vision_RK3588.rknn) |
|
| 35 |
+
|
| 36 |
+
## Run on your board
|
| 37 |
+
|
| 38 |
+
Download the RK3576 W4A16-g128 language model and matching encoder:
|
| 39 |
+
|
| 40 |
+
```bash
|
| 41 |
+
hf download HanzoHuang/Qwen3-VL-4B-Instruct-RKLLM \
|
| 42 |
+
v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm \
|
| 43 |
+
v1.3.1/RK3576/Qwen3-VL-4B-Instruct_vision_RK3576.rknn \
|
| 44 |
+
--local-dir ./models
|
| 45 |
+
|
| 46 |
+
sudo docker run --rm -it --pull=always --privileged \
|
| 47 |
+
-p 8001:8001 -v /dev:/dev \
|
| 48 |
+
-v "$PWD/models/v1.3.1/RK3576:/app/models:ro" \
|
| 49 |
+
-e MODEL_KIND=vlm -e TARGET_PLATFORM=rk3576 -e RKLLM_EMBED_FLASH=1 \
|
| 50 |
+
-e MODEL_FILE=Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm \
|
| 51 |
+
-e VISION_MODEL_FILE=Qwen3-VL-4B-Instruct_vision_RK3576.rknn \
|
| 52 |
+
ghcr.io/hanzo-huang/rkllm-docker:env-latest \
|
| 53 |
+
serve --max_context_len 4096
|
| 54 |
+
```
|
| 55 |
+
|
| 56 |
+
For RK3588, download both RK3588 files, mount that directory, set `TARGET_PLATFORM=rk3588`, and select its W8A8 language filename. Use the [runtime documentation](https://github.com/Hanzo-Huang/rkllm-docker) for health checks and OpenAI-compatible image requests.
|
| 57 |
+
|
| 58 |
+
## Release notes
|
| 59 |
+
|
| 60 |
+
- Source acquired from ModelScope `Qwen/Qwen3-VL-4B-Instruct`, pinned revision `0196dc7bb23f3c742616147c3254d0e4f1207787`. The HF link above identifies the canonical upstream model; no equivalence to an HF Git commit is assumed.
|
| 61 |
+
- Export context: **8192 tokens**. Functional API tests used **4096**; the speed/memory benchmark used **2048**. Longer runtime context has not been validated on these boards.
|
| 62 |
+
- Fixed vision input: RGB, 448 × 448, batch 1, 196 image tokens, mean/std 0.5. The runtime pads to a square using RGB 127, then resizes bilinearly. Vision output order is pooler followed by three deepstack features for each token.
|
| 63 |
+
- Language calibration: the SDK's 20 image-and-text examples, captured using the source processor and language-model input hook. W4A16-g128 uses GRQ; W8A8 uses normal quantization. Float32 CUDA loading; two NPU cores on RK3576 and three on RK3588.
|
| 64 |
+
- Export compatibility fixes select the four language-width vision outputs under Transformers 5 and import only the requested calibration model family. Generated upstream examples and source checkpoint weights are unchanged.
|
| 65 |
+
- All three variants passed seven API smoke checks on their target board: arithmetic, independent requests, SSE, exact conversation recall, image description, image color over SSE, and text after an image. Tests used the baked RKLLM 1.3.1 runtime application with `embed_flash=1`.
|
| 66 |
+
- RK3576 vision encoder cosine against original float32 embeddings: 0.99534508.
|
| 67 |
+
- RK3588 vision encoder cosine against original float32 embeddings: 0.99543323.
|
| 68 |
+
|
| 69 |
+
These are functional and numerical smoke checks, not a broad quality benchmark. Quantization and fixed image resolution may reduce accuracy.
|
| 70 |
+
|
| 71 |
+
Measured on the target boards:
|
| 72 |
+
|
| 73 |
+
| Board | Quantization | Decode tok/s | Startup RAM (GB) | Stable RAM (GB) | Date |
|
| 74 |
+
| --- | --- | ---: | ---: | ---: | --- |
|
| 75 |
+
| RK3576 | w4a16-g128 | 5.51 | 4.01 | 4.01 | 2026-10-23 |
|
| 76 |
+
| RK3576 | w8a8 | 4.11 | 5.83 | 5.83 | 2026-10-23 |
|
| 77 |
+
| RK3588 | w8a8 | 6.58 | 5.58 | 5.62 | 2026-10-22 |
|
| 78 |
+
|
| 79 |
+
Speed is the median native decode rate over three 128-token runs after a 32-token warmup, greedy decoding, EOS ignored, context 2048. RAM is additional board usage above a five-second idle baseline: sampled loading peak and ten-second ready-state median, in decimal GB. The matched encoder is loaded; `embed_flash=1`. First-token timing stays in the private report. Measurements depend on the board, clocks, cooling, and background load.
|
| 80 |
+
|
| 81 |
+
RK3576 measurements temporarily disabled zram swap and restored it after each run; both had zero swap growth. RK3588 retained its normal swap configuration.
|
| 82 |
+
|
| 83 |
+
<details>
|
| 84 |
+
<summary>SHA-256 checksums</summary>
|
| 85 |
+
|
| 86 |
+
```text
|
| 87 |
+
0a014ecf5f8513878e4c9fefd6c4ceec8f105005d2920588dbd5ded188a6c421 v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm
|
| 88 |
+
b4a132c43fa10a5ce86b81f100a1cfa6f0aa1ffbc477b20e32a38d4f64b50a40 v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w8a8.rkllm
|
| 89 |
+
c8074176816c68ab635c70a7a0db3044a15f0f7b272fc45c0e6cc68875db159e v1.3.1/RK3576/Qwen3-VL-4B-Instruct_vision_RK3576.rknn
|
| 90 |
+
cbf7b3bb2eec550f268fbc64fadcf53791283acdfba48fe0ff621aa46aba884b v1.3.1/RK3588/Qwen3-VL-4B-Instruct_RK3588_w8a8.rkllm
|
| 91 |
+
478e5922e5a28d130146bad9b22dfc66b4eeb53880477744bc3130eab1fb321d v1.3.1/RK3588/Qwen3-VL-4B-Instruct_vision_RK3588.rknn
|
| 92 |
+
```
|
| 93 |
+
|
| 94 |
+
</details>
|
| 95 |
+
|
| 96 |
+
## Credits
|
| 97 |
+
|
| 98 |
+
[Qwen](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) provides the original model. Conversion uses [Rockchip RKLLM](https://github.com/airockchip/rknn-llm/tree/release-v1.3.1), [RKNN Toolkit2](https://github.com/airockchip/rknn-toolkit2), and the [conversion wrapper](https://github.com/Hanzo-Huang/rknn-llm).
|
SHA256SUMS
ADDED
|
@@ -0,0 +1,5 @@
|
|
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|
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|
|
| 1 |
+
0a014ecf5f8513878e4c9fefd6c4ceec8f105005d2920588dbd5ded188a6c421 v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm
|
| 2 |
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b4a132c43fa10a5ce86b81f100a1cfa6f0aa1ffbc477b20e32a38d4f64b50a40 v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w8a8.rkllm
|
| 3 |
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c8074176816c68ab635c70a7a0db3044a15f0f7b272fc45c0e6cc68875db159e v1.3.1/RK3576/Qwen3-VL-4B-Instruct_vision_RK3576.rknn
|
| 4 |
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cbf7b3bb2eec550f268fbc64fadcf53791283acdfba48fe0ff621aa46aba884b v1.3.1/RK3588/Qwen3-VL-4B-Instruct_RK3588_w8a8.rkllm
|
| 5 |
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478e5922e5a28d130146bad9b22dfc66b4eeb53880477744bc3130eab1fb321d v1.3.1/RK3588/Qwen3-VL-4B-Instruct_vision_RK3588.rknn
|
v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w4a16_g128.rkllm
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:0a014ecf5f8513878e4c9fefd6c4ceec8f105005d2920588dbd5ded188a6c421
|
| 3 |
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size 3393227652
|
v1.3.1/RK3576/Qwen3-VL-4B-Instruct_RK3576_w8a8.rkllm
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:b4a132c43fa10a5ce86b81f100a1cfa6f0aa1ffbc477b20e32a38d4f64b50a40
|
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size 4964793348
|
v1.3.1/RK3576/Qwen3-VL-4B-Instruct_vision_RK3576.rknn
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:c8074176816c68ab635c70a7a0db3044a15f0f7b272fc45c0e6cc68875db159e
|
| 3 |
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size 890353821
|
v1.3.1/RK3588/Qwen3-VL-4B-Instruct_RK3588_w8a8.rkllm
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:cbf7b3bb2eec550f268fbc64fadcf53791283acdfba48fe0ff621aa46aba884b
|
| 3 |
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size 4852746836
|
v1.3.1/RK3588/Qwen3-VL-4B-Instruct_vision_RK3588.rknn
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:478e5922e5a28d130146bad9b22dfc66b4eeb53880477744bc3130eab1fb321d
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size 869260061
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