321 lines
10 KiB
Markdown
321 lines
10 KiB
Markdown
# Qwen3-VL-Reranker
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## 1 Introduction
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The Qwen3-VL-Embedding and Qwen3-VL-Reranker model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities. This guide describes how to run the model with vLLM Ascend.
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## 2 Supported Features
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Refer to [supported features](../../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
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## 3 Prerequisites
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### 3.1 Model Weight
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- `Qwen3-VL-Reranker-8B` [Download model weight](https://www.modelscope.cn/models/Qwen/Qwen3-VL-Reranker-8B)
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- `Qwen3-VL-Reranker-2B` [Download model weight](https://www.modelscope.cn/models/Qwen/Qwen3-VL-Reranker-2B)
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It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
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## 4 Installation
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### 4.1 Docker Image Installation
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You can use our official docker image to run `Qwen3-VL-Reranker` model directly.
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Select an image based on your machine type and start the docker image on your node, refer to [using docker](../../installation.md#set-up-using-docker).
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For Atlas 300I DUO, use `vllm-ascend:nightly-releases-v0.23.0-310p` (or a later `-310p` image).
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:::::{tab-set}
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:sync-group: install
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::::{tab-item} A3 series
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:sync: A3 series
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```{code-block} bash
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:substitutions:
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export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3
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docker run --rm \
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--name vllm-ascend \
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--shm-size=1g \
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--net=host \
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--privileged=true \
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--device /dev/davinci0 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-it $IMAGE bash
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```
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::::
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::::{tab-item} A2 series
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:sync: A2 series
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```{code-block} bash
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:substitutions:
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export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
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docker run --rm \
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--name vllm-ascend \
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--shm-size=1g \
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--net=host \
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--privileged=true \
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--device /dev/davinci0 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-it $IMAGE bash
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```
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::::
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::::{tab-item} Atlas 300I DUO
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:sync: Atlas 300I DUO
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```{code-block} bash
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:substitutions:
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export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p
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docker run --rm \
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--name vllm-ascend \
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--shm-size=1g \
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--net=host \
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--privileged=true \
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--device /dev/davinci0 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-it $IMAGE bash
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```
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::::
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:::::
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After a successful docker run, you can verify the running container service by executing the `docker ps` command.
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### 4.2 Source Code Installation
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If you don't want to use the docker image as above, you can also build all from source:
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- Install `vllm-ascend` from source, refer to [installation](../../installation.md).
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If you want to deploy multi-node environment, you need to set up environment on each node.
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## 5 Online Service Deployment
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### 5.1 Chat Template
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The Qwen3-VL-Reranker model requires a specific chat template for proper formatting. Create a file named `qwen3_vl_reranker.jinja` with the following content:
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```jinja
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<|im_start|>system
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Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>
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<|im_start|>user
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<Instruct>: {{
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messages
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| selectattr("role", "eq", "system")
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| map(attribute="content")
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| first
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| default("Given a search query, retrieve relevant candidates that answer the query.")
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}}<Query>:{{
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messages
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| selectattr("role", "eq", "query")
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| map(attribute="content")
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| first
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}}
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<Document>:{{
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messages
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| selectattr("role", "eq", "document")
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| map(attribute="content")
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| first
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}}<|im_end|>
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<|im_start|>assistant
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```
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Save this file to a location of your choice (e.g., `./qwen3_vl_reranker.jinja`).
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:::::{tab-set}
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:sync-group: Deployment
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::::{tab-item} A3/A2 series
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:sync: A3/A2 series
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```{code-block} bash
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:substitutions:
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vllm serve Qwen/Qwen3-VL-Reranker-2B \
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--served-model-name Qwen/Qwen3-VL-Reranker-2B \
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--runner pooling \
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--hf_overrides '{"architectures": ["Qwen3VLForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}' \
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--chat-template ./qwen3_vl_reranker.jinja \
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--port 8000 \
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--max-model-len 1024
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```
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::::{tab-item} Atlas 300I DUO
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:sync: Atlas 300I DUO
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Start the docker image on each node.
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```{code-block} bash
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:substitutions:
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vllm serve Qwen/Qwen3-VL-Reranker-2B \
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--served-model-name Qwen/Qwen3-VL-Reranker-2B \
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--runner pooling \
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--hf_overrides '{"architectures": ["Qwen3VLForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}' \
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--chat-template ./qwen3_vl_reranker.jinja \
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--compilation-config '{"cudagraph_capture_sizes": [1024,512]}' \
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--additional-config '{"ascend_compilation_config": {"fuse_norm_quant": false}}' \
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--dtype float16 \
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--port 8000 \
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--max-model-len 1024
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```
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Required Parameter Descriptions:
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`--compilation-config` For Atlas 300I DUO, due to limited hardware streams, the size of cudagraph_capture_sizes is restricted.
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Key Parameter Descriptions:
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- `--max-model-len` represents the context length, which is the maximum value of the input plus output for a single request. For Atlas 300I DUO if automatic parsing resolves to a large context length, allocating this mask (O(max_model_len^2)) may exceed NPU memory and trigger OOM. Be sure to set an explicit and conservative value, such as --max-model-len 1024.
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Common Issues Tip: If you encounter issues, please refer to the [Public FAQ](https://docs.vllm.ai/projects/ascend/en/latest/faqs.html) for troubleshooting.
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## 6 Functional Verification
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Once your server is started, you can verify by follow command:
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Service Verification:
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```bash
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curl http://localhost:8000/v1/rerank \
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-X POST \
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-d '{"query":"What is the capital of China?", "documents": ["The capital of China is Beijing.", "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."]}' \
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-H 'Content-Type: application/json'
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```
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Expected Result:
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The service returns HTTP 200 OK with a JSON response containing the `relevance_score` field. Example output:
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```json
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{
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"id": "score-xxxxx",
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"model": "Qwen/Qwen3-VL-Reranker-2B",
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"usage": {
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"prompt_tokens": 179,
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"total_tokens": 179
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},
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"results": [
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{
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"index": 0,
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"document": {
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"text": "The capital of China is Beijing.",
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"multi_modal": null
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},
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"relevance_score": 0.7209711670875549
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},
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{
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"index": 1,
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"document": {
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"text": "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
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"multi_modal": null
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},
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"relevance_score": 0.18871910870075226
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}
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]
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}
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```
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For more usage examples, please reference the [examples](https://github.com/vllm-project/vllm/tree/main/examples/pooling/score)
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## 7 Accuracy Evaluation
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Here are two accuracy evaluation methods.
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### Using MTEB
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1. Refer to [MTEB](https://docs.mteb.org/) for details.
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2. Run follow code to execute the accuracy evaluation.
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```python
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import os
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from mteb.models.vllm_wrapper import VllmCrossEncoderWrapper
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if __name__ == "__main__":
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import mteb
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data_path = "/home/data/mteb_data"
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os.environ["HF_DATASETS_CACHE"] = data_path
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os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
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model = VllmCrossEncoderWrapper(f"/home/data/Qwen3-VL-Reranker-2B",
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revision="norm",
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dtype="float16",
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enforce_eager=True,
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max_model_len=10240,
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hf_overrides={"architectures": ["Qwen3VLForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": True})
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cache = mteb.ResultCache("/home/data/mteb_data")
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tasks = mteb.get_tasks(
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task_types=["Reranking"],
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languages=["zho"]
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)
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tasks = mteb.get_tasks(tasks=["MultiLongDocReranking"])
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results = mteb.evaluate(model, tasks=tasks, cache=cache, overwrite_strategy="always")
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print(results)
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```
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3. After execution, you can get the result.
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## 8 Performance Evaluation
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### Using vLLM Benchmark
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Run performance of `Qwen3-VL-Reranker-2B` as an example.
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Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/benchmarking/cli/) for more details.
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Take the `serve` as an example. Run the code as follows.
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```bash
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vllm bench serve --model Qwen/Qwen3-VL-Reranker-2B --backend vllm-rerank --port 8000 --dataset-name random-rerank --endpoint /v1/rerank --random-input 200 --save-result --result-dir ./
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```
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After about several minutes, you can get the performance evaluation result.
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## 9 FAQ
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For common environment, installation, and general parameter issues, please refer to the [Public FAQ](https://docs.vllm.ai/projects/ascend/en/latest/faqs.html).
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