Release validated RKLLM 1.3.1 VLM artifacts

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w4a16_g128.rkllm filter=lfs diff=lfs merge=lfs -text
+v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w8a8.rkllm filter=lfs diff=lfs merge=lfs -text
+v1.3.1/RK3576/Qwen3-VL-2B-Instruct_vision_RK3576.rknn filter=lfs diff=lfs merge=lfs -text
+v1.3.1/RK3588/Qwen3-VL-2B-Instruct_RK3588_w8a8.rkllm filter=lfs diff=lfs merge=lfs -text
+v1.3.1/RK3588/Qwen3-VL-2B-Instruct_vision_RK3588.rknn filter=lfs diff=lfs merge=lfs -text

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-VL-2B-Instruct
+library_name: rkllm
+pipeline_tag: image-text-to-text
+tags: [rkllm, rockchip, rk3576, rk3588, qwen3-vl]
+---
+
+# Qwen3-VL-2B-Instruct · RKLLM
+
+Text and image chat on Rockchip RK3576 and RK3588, converted from [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct).
+
+- **Conversion toolkit:** RKLLM Toolkit 1.3.1 and RKNN Toolkit2 2.5.0.
+- **Inputs / output:** text and one image → text. Video and audio input are unavailable in this runtime.
+- **License:** Apache 2.0; see the [original model](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct) and [license](LICENSE).
+
+## Choose your files
+
+Use files for your board's exact SoC. RK3576 offers compact W4A16-g128 and W8A8; RK3588 uses W8A8. Download size is not runtime RAM.
+
+| Board | Quantization | Size | Language model |
+| --- | --- | --- | --- |
+| RK3576 | W4A16-g128 | 1.74 GiB | [Qwen3-VL-2B-Instruct_RK3576_w4a16_g128.rkllm](v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w4a16_g128.rkllm) |
+| RK3576 | w8a8 | 2.28 GiB | [Qwen3-VL-2B-Instruct_RK3576_w8a8.rkllm](v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w8a8.rkllm) |
+| RK3588 | w8a8 | 2.22 GiB | [Qwen3-VL-2B-Instruct_RK3588_w8a8.rkllm](v1.3.1/RK3588/Qwen3-VL-2B-Instruct_RK3588_w8a8.rkllm) |
+
+Every language model requires its board's matching vision encoder below. Both RK3576 language variants share the RK3576 encoder.
+
+| Board | Size | Vision encoder |
+| --- | --- | --- |
+| RK3576 | 0.81 GiB | [Qwen3-VL-2B-Instruct_vision_RK3576.rknn](v1.3.1/RK3576/Qwen3-VL-2B-Instruct_vision_RK3576.rknn) |
+| RK3588 | 0.80 GiB | [Qwen3-VL-2B-Instruct_vision_RK3588.rknn](v1.3.1/RK3588/Qwen3-VL-2B-Instruct_vision_RK3588.rknn) |
+
+## Run on your board
+
+Download the RK3576 W4A16-g128 language model and matching encoder:
+
+```bash
+hf download HanzoHuang/Qwen3-VL-2B-Instruct-RKLLM \
+  v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w4a16_g128.rkllm \
+  v1.3.1/RK3576/Qwen3-VL-2B-Instruct_vision_RK3576.rknn \
+  --local-dir ./models
+
+sudo docker run --rm -it --pull=always --privileged \
+  -p 8001:8001 -v /dev:/dev \
+  -v "$PWD/models/v1.3.1/RK3576:/app/models:ro" \
+  -e MODEL_KIND=vlm -e TARGET_PLATFORM=rk3576 -e RKLLM_EMBED_FLASH=1 \
+  -e MODEL_FILE=Qwen3-VL-2B-Instruct_RK3576_w4a16_g128.rkllm \
+  -e VISION_MODEL_FILE=Qwen3-VL-2B-Instruct_vision_RK3576.rknn \
+  ghcr.io/hanzo-huang/rkllm-docker:env-latest \
+  serve --max_context_len 4096
+```
+
+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.
+
+## Release notes
+
+- Source acquired from ModelScope `Qwen/Qwen3-VL-2B-Instruct`, pinned revision `ae9985b208c074c10cfbe3a61b5cb7268cdc9c53`. The HF link above identifies the canonical upstream model; no equivalence to an HF Git commit is assumed.
+- 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.
+- 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.
+- 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.
+- 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.
+- 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`.
+- RK3576 vision encoder cosine against original float32 embeddings: 0.99767705.
+- RK3588 vision encoder cosine against original float32 embeddings: 0.99805251.
+
+These are functional and numerical smoke checks, not a broad quality benchmark. Quantization and fixed image resolution may reduce accuracy.
+
+Measured on 2026-10-22:
+
+| Board | Quantization | Decode tok/s | Startup RAM (GB) | Stable RAM (GB) |
+| --- | --- | ---: | ---: | ---: |
+| RK3576 | w4a16-g128 | 11.11 | 2.60 | 2.57 |
+| RK3576 | w8a8 | 9.02 | 3.18 | 3.18 |
+| RK3588 | w8a8 | 14.05 | 3.03 | 3.07 |
+
+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.
+
+RK3576 W8A8 was measured with zram swap temporarily disabled and restored afterward. Earlier measurements with background swap growth were excluded.
+
+<details>
+<summary>SHA-256 checksums</summary>
+
+```text
+f687dccc90da6ef74d5308af3a05dba5cf3fea68c359b151381b7dbdc8a06c8f  v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w4a16_g128.rkllm
+77ae22a5d1dd8c15833c57e390de4afc3aa4f1867c5d24b0a16e32e8d1e20adf  v1.3.1/RK3576/Qwen3-VL-2B-Instruct_RK3576_w8a8.rkllm
+5ec488c125b6604cd623913ce5126db3f5075f5e1412f8d8f912ca14c2513dbc  v1.3.1/RK3576/Qwen3-VL-2B-Instruct_vision_RK3576.rknn
+e8fff0de10e2f48ba13af759faca3a44cc07f661e95aeca412850a7eba08a92e  v1.3.1/RK3588/Qwen3-VL-2B-Instruct_RK3588_w8a8.rkllm
+e90e160835c1a4d3d77095954aa518da513179ee0692b1bcc7a73f971f74d5a2  v1.3.1/RK3588/Qwen3-VL-2B-Instruct_vision_RK3588.rknn
+```
+
+</details>
+
+## Credits
+
+[Qwen](https://huggingface.co/Qwen/Qwen3-VL-2B-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).

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