Qwen3-VL-Embedding

Qwen3-VL-Embedding#

1 Introduction#

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.

2 Supported Features#

Refer to supported features to get the model’s supported feature matrix.

3 Prerequisites#

3.1 Model Weight#

Qwen3-VL-Embedding-8B Download model weight
Qwen3-VL-Embedding-2B Download model weight

It is recommended to download the model weight to the shared directory of multiple nodes, such as /root/.cache/

4 Installation#

4.1 Docker Image Installation#

You can use our official docker image to run Qwen3-VL-Embedding model directly.

Select an image based on your machine type and start the docker image on your node, refer to using docker.

A3 series

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0.post1-a3
docker run --rm \
  --name vllm-ascend \
  --shm-size=1g \
  --net=host \
  --privileged=true \
  --device /dev/davinci0 \
  --device /dev/davinci_manager \
  --device /dev/devmm_svm \
  --device /dev/hisi_hdc \
  -v /usr/local/dcmi:/usr/local/dcmi \
  -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
  -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
  -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
  -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
  -v /etc/ascend_install.info:/etc/ascend_install.info \
  -v /root/.cache:/root/.cache \
  -it $IMAGE bash

A2 series

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0.post1
docker run --rm \
  --name vllm-ascend \
  --shm-size=1g \
  --net=host \
  --privileged=true \
  --device /dev/davinci0 \
  --device /dev/davinci_manager \
  --device /dev/devmm_svm \
  --device /dev/hisi_hdc \
  -v /usr/local/dcmi:/usr/local/dcmi \
  -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
  -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
  -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
  -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
  -v /etc/ascend_install.info:/etc/ascend_install.info \
  -v /root/.cache:/root/.cache \
  -it $IMAGE bash

Atlas 300I DUO

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0.post1-310p
docker run --rm \
  --name vllm-ascend \
  --shm-size=1g \
  --net=host \
  --privileged=true \
  --device /dev/davinci0 \
  --device /dev/davinci_manager \
  --device /dev/devmm_svm \
  --device /dev/hisi_hdc \
  -v /usr/local/dcmi:/usr/local/dcmi \
  -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
  -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
  -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
  -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
  -v /etc/ascend_install.info:/etc/ascend_install.info \
  -v /root/.cache:/root/.cache \
  -it $IMAGE bash

After a successful docker run, you can verify the running container service by executing the docker ps command.

4.2 Source Code Installation#

If you don’t want to use the docker image as above, you can also build all from source:

Install vllm-ascend from source, refer to installation.

If you want to deploy multi-node environment, you need to set up environment on each node.

5 Online Service Deployment#

A3/A2 series

vllm serve Qwen/Qwen3-VL-Embedding-2B  \
   --served-model-name Qwen/Qwen3-VL-Embedding-2B  \
   --runner pooling \
   --port 8000 \
   --max-model-len 1024

Atlas 300I DUO

vllm serve Qwen/Qwen3-VL-Embedding-2B  \
   --served-model-name Qwen/Qwen3-VL-Embedding-2B  \
   --compilation-config '{"cudagraph_capture_sizes": [1024,512]}' \
   --additional-config '{"ascend_compilation_config": {"fuse_norm_quant": false}}' \
   --runner pooling \
   --dtype float16 \
   --port 8000 \
   --max-model-len 1024

Required Parameter Descriptions:

--compilation-config For Atlas 300I DUO, due to limited hardware streams, the size of cudagraph_capture_sizes is restricted.

Key Parameter Descriptions:

--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.

Common Issues Tip: If you encounter issues, please refer to the Public FAQ for troubleshooting.

6 Functional Verification#

Once your server is started, you can verify by follow command:

Service Verification:

curl -X POST http://localhost:8000/v1/embeddings -H "Content-Type: application/json" -d '{
  "input": [
        "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."
    ]
}'

Expected Result:

The service returns HTTP 200 OK with a JSON response containing the embedding field. Example output:

{
  "id": "embd-8136155c01e8411d",
  "object": "list",
  "created": 1784538286,
  "model": "Qwen/Qwen3-VL-Embedding-2B",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        -0.028474265709519386,
        -0.02678542211651802
      ]
    },
    {
      "index": 1,
      "object": "embedding",
      "embedding": [
        -0.016785264015197754,
        -0.003787524998188019
      ]
    }
  ],
  "usage": {
    "prompt_tokens": 39,
    "total_tokens": 39,
    "completion_tokens": 0,
    "prompt_tokens_details": null
  }
}

For more usage examples, please reference the examples

7 Accuracy Evaluation#

Here are two accuracy evaluation methods.

Using MTEB#

Refer to MTEB for details.

Run follow code to execute the accuracy evaluation.

import os
import mteb

from mteb.models.vllm_wrapper import VllmEncoderWrapper

if __name__ == "__main__":

    data_path = "/home/data/mteb_data"
    os.environ["HF_DATASETS_CACHE"] = data_path
    os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"

    model = VllmEncoderWrapper(f"/root/.cache/Qwen3-VL-Embedding-2B",
                                revision="norm",
                                dtype="float16",
                                max_model_len=10240,
                               )

    cache = mteb.ResultCache("/home/data/mteb_data")
    tasks = mteb.get_tasks(tasks=["LeCaRDv2"])
    results = mteb.evaluate(model, tasks=tasks, cache=cache, encode_kwargs={"batch_size": 2}, overwrite_strategy="always")
    df = results.to_dataframe()
    print(df)

After execution, you can get the result.

8 Performance Evaluation#

Using vLLM Benchmark#

Run performance of Qwen3-VL-Embedding-2B as an example. Refer to vllm benchmark for more details.

Take the serve as an example. Run the code as follows.

vllm bench serve --model Qwen/Qwen3-VL-Embedding-2B --backend openai-embeddings --port 8000 --dataset-name random --endpoint /v1/embeddings --random-input 200 --save-result --result-dir ./

After about several minutes, you can get the performance evaluation result.

9 FAQ#

For common environment, installation, and general parameter issues, please refer to the Public FAQ.