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# Qwen3-TTS
## 论文
[Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking](https://arxiv.org/abs/2601.04720)
## 模型简介
Qwen3-VL-Embedding 和 Qwen3-VL-Reranker 模型系列是通义千问（Qwen）家族的最新成员，基于近期开源且强大的 Qwen3-VL 基础模型构建而成。该系列专为多模态信息检索与跨模态理解而设计，可接受包括文本、图像、截图和视频在内的多种输入形式，以及这些模态任意组合的混合输入。 Embedding 模型可生成高维向量，适用于检索、聚类等广泛场景；而 Reranker 模型则用于对初步结果进行精细化排序，二者共同构建了一套完整的前沿多模态搜索流水线。
多模态通用性：两个模型均能在统一框架下无缝处理文本、图像、截图和视频等多种输入，在图像-文本检索、视频-文本匹配、视觉问答（VQA）以及多模态内容聚类等多样化任务中均达到业界领先水平。
统一表征学习（Embedding）：依托 Qwen3-VL 架构，Embedding 模型在共享语义空间中生成同时包含视觉与文本信息的丰富向量，从而高效支持跨模态的相似度计算与检索。
高精度重排序（Reranker）：我们还推出了 Qwen3-VL-Reranker 系列模型，以补充 Embedding 模型的能力。Reranker 接收一个（查询，文档）对作为输入——其中查询和文档均可包含任意单一或混合模态——并输出精确的相关性分数。在检索流程中，通常先由 Embedding 模型执行高效的初步召回，再由 Reranker 在后续阶段对结果进行精细化重排序。这种两阶段方法显著提升了检索准确率。
卓越的实用性：继承 Qwen3-VL 的多语言能力，该系列支持 30 多种语言，非常适合全球化应用。其在实际场景中高度实用，提供灵活的向量维度、针对特定用例可定制的指令支持，即使使用量化后的嵌入向量也能保持强劲性能。这些特性使开发者能够轻松将两个模型集成到现有系统中，实现强大的跨语言与跨模态理解能力。
<div align=center>
<img src="./doc/01.png"/>
</div>
## 环境依赖
| 软件 | 版本 |
| :------: | :------: |
| DTK | 25.04.2 |
| python | 3.10.12 |
| transformers | 4.57.6 |
| vllm | 0.11.0+das.opt1.rc2.dtk2604 |
推荐使用镜像:harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk26.04-0130-py3.10-20260202
```bash
docker run -it \
--shm-size 60g \
--network=host \
--name qwen3-tts \
--privileged \
--device=/dev/kfd \
--device=/dev/dri \
--device=/dev/mkfd \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-u root \
-v /opt/hyhal/:/opt/hyhal/:ro \
-v /path/your_code_data/:/path/your_code_data/ \
harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.11.0-ubuntu22.04-dtk26.04-0130-py3.10-20260202 bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装
镜像内其他环境配置
```
1.解压vllm.zip到/usr/local/lib/python3.10/dist-packages直接覆盖需要修改的文件
unzip -o vllm.zip -d /usr/local/lib/python3.10/dist-packages
```
## 数据集
暂无
## 训练
暂无
## 推理
### vllm
#### 单机推理
启动服务
```bash
vllm serve Qwen/Qwen3-VL-Embedding-8B --runner pooling --host 0.0.0.0 --port 8000 --served-model-name qwen3-vl-embedding --max-model-len 8192 --gpu-memory-utilization 0.95
```
调用服务：
```
curl -s http://127.0.0.1:8000/v1/embeddings -H "Content-Type: application/json" -d '{"model": "qwen3-vl-embedding","input": "这是一个用于测试 vLLM embedding 服务是否正常的句子。"}'
```
## 效果展示
![alt text](doc/image.png)
### 精度
`DCU与GPU精度一致，推理框架：vllm`
## 预训练权重
| 模型名称 | 权重大小 | DCU型号 | 最低卡数需求 |下载地址|
|:-----:|:----------:|:----------:|:---------------------:|:----------:|
| Qwen3-VL-Embedding-8B | 8B | K100AI | 1 | [Modelscope](https://www.modelscope.cn/models/Qwen/Qwen3-VL-Embedding-8B)|
| Qwen3-VL-Embedding-2B | 2B | K100AI | 1 | [Modelscope](https://www.modelscope.cn/models/Qwen/Qwen3-VL-Embedding-2B)|
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/qwen3-vl-embedding
## 参考资料
- https://github.com/QwenLM/Qwen3-VL-Embedding
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<p align="center">
<img src="https://model-demo.oss-cn-hangzhou.aliyuncs.com/Qwen3-VL-Embedding.png" width="400"/>
<img src="https://model-demo.oss-cn-hangzhou.aliyuncs.com/Qwen3-VL-Reranker.png" width="400"/>
</p>
# Qwen3-VL-Embedding & Qwen3-VL-Reranker
<!-- Badges section -->
[![GitHub](https://img.shields.io/badge/GitHub-black?logo=github)](https://github.com/QwenLM/Qwen3-VL-Embedding)
[![Hugging Face - Embedding](https://img.shields.io/badge/🤗-Embedding-yellow)](https://huggingface.co/collections/Qwen/qwen3-vl-embedding)
[![Hugging Face - Reranker](https://img.shields.io/badge/🤗-Reranker-yellow)](https://huggingface.co/collections/Qwen/qwen3-vl-reranker)
[![ModelScope - Embedding](https://img.shields.io/badge/ModelScope-Embedding-blue)](https://modelscope.cn/organization/qwen/qwen3-vl-embedding)
[![ModelScope - Reranker](https://img.shields.io/badge/ModelScope-Reranker-blue)](https://modelscope.cn/organization/qwen/qwen3-vl-reranker)
[![Technical Report](https://img.shields.io/badge/📄-Technical%20Report-red)](assets/qwen3vlembedding_technical_report.pdf)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
<!-- Brief description -->
**State-of-the-art multimodal embedding and reranking models built on Qwen3-VL, supporting text, images, screenshots, videos, and mixed-modal inputs for advanced information retrieval and cross-modal understanding.**
---
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Model Architecture](#model-architecture)
- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Model Performance](#model-performance)
- [Citation](#citation)
---
## Overview
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](https://huggingface.co/collections/Qwen/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.
Building on the success of our text-oriented [Qwen3-Embedding](https://huggingface.co/collections/Qwen/qwen3-embedding) and [Qwen3-Reranker](https://huggingface.co/collections/Qwen/qwen3-reranker) series, these multimodal models extend best-in-class performance to visual and video understanding tasks. The models work in tandem: the Embedding model handles the initial recall stage by generating semantically rich vectors, while the Reranking model manages the re-ranking stage with precise relevance scoring, significantly enhancing final retrieval accuracy.
---
## Features
- **🎨 Multimodal Versatility**: Seamlessly process inputs containing text, images, screenshots, and video within a unified framework. Achieve state-of-the-art performance across diverse tasks including image-text retrieval, video-text matching, visual question answering (VQA), and multimodal content clustering.
- **🔄 Unified Representation Space**: Leverage the Qwen3-VL architecture to generate semantically rich vectors that capture both visual and textual information in a shared space, facilitating efficient similarity estimation and retrieval across different modalities.
- **🎯 High-Precision Reranking**: The reranking model accepts input pairs (Query, Document)—where both can consist of arbitrary single or mixed modalities—and outputs precise relevance scores for superior retrieval accuracy.
- **🌍 Exceptional Practicality**:
- Support for over 30 languages, ideal for global applications
- Customizable instructions for task-specific optimization
- Flexible vector dimensions with Matryoshka Representation Learning (MRL)
- Strong performance with quantized embeddings for efficient deployment
- Easy integration into existing retrieval pipelines
---
## Model Architecture
### Model Specifications
| Model | Size | Layers | Sequence Length | Embedding Dimension | Quantization Support | MRL Support | Instruction Aware |
|---|---|---|---|---|---|---|---|
| **Qwen3-VL-Embedding-2B** | 2B | 28 | 32K | 2048 | ✅ | ✅ | ✅ |
| **Qwen3-VL-Embedding-8B** | 8B | 36 | 32K | 4096 | ✅ | ✅ | ✅ |
| **Qwen3-VL-Reranker-2B** | 2B | 28 | 32K | - | - | - | ✅ |
| **Qwen3-VL-Reranker-8B** | 8B | 36 | 32K | - | - | - | ✅ |
### LoRA Configs
| Model | rank | alpha | target_modules |
|------|------|-------|----------------|
| Qwen3-VL-Embedding | 32 | 32 | q_proj v_proj k_proj up_proj down_proj gate_proj |
| Qwen3-VL-Reranker | 32 | 32 | q_proj v_proj k_proj up_proj down_proj gate_proj |
### Architecture Design
**Qwen3-VL-Embedding: Dual-Tower Architecture**
- Receives single-modal or mixed-modal input and maps it into a high-dimensional semantic vector
- Extracts the hidden state vector corresponding to the `[EOS]` token from the base model's last layer as the final semantic representation
- Enables efficient, independent encoding necessary for large-scale retrieval
**Qwen3-VL-Reranker: Single-Tower Architecture**
- Receives an input pair `(Query, Document)` and performs pointwise reranking
- Utilizes Cross-Attention mechanism for deeper, finer-grained inter-modal interaction and information fusion
- Expresses relevance score by predicting the generation probability of special tokens (`yes` and `no`)
### Feature Comparison
| | Qwen3-VL-Embedding | Qwen3-VL-Reranker |
|---------|-------------------|-------------------|
| **Core Function** | Semantic Representation, Embedding Generation | Relevance Scoring, Pointwise Re-ranking |
| **Input** | Single modality or mixed modalities | (Query, Document) pair with single- or mixed-modal inputs |
| **Architecture** | Dual-Tower | Single-Tower |
| **Mechanism** | Efficient Retrieval | Deep Inter-Modal Interaction, Precise Alignment |
| **Output** | Semantic Vector | Relevance Score |
Both models are built through a multi-stage training paradigm that fully leverages the powerful general multimodal semantic understanding capabilities of Qwen3-VL, providing high-quality semantic representations and precise re-ranking mechanisms for complex, large-scale multimodal retrieval tasks.
---
## Installation
### Setup Environment
```bash
# Clone the repository
git clone https://github.com/QwenLM/Qwen3-VL-Embedding.git
cd Qwen3-VL-Embedding
# Run the script to setup the environment
bash scripts/setup_environment.sh
```
The setup script will automatically:
- Install `uv` if not already installed
- Install all project dependencies
After setup completes, activate the environment:
```bash
source .venv/bin/activate
```
### Download Models
Our models are available on both Hugging Face and ModelScope.
| Model | Hugging Face | ModelScope |
|-------|--------------|------------|
| Qwen3-VL-Embedding-2B |[Link](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) | [Link](https://modelscope.cn/models/qwen/Qwen3-VL-Embedding-2B) |
| Qwen3-VL-Embedding-8B |[Link](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) | [Link](https://modelscope.cn/models/qwen/Qwen3-VL-Embedding-8B) |
| Qwen3-VL-Reranker-2B |[Link](https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B) | [Link](https://modelscope.cn/models/qwen/Qwen3-VL-Reranker-2B) |
| Qwen3-VL-Reranker-8B |[Link](https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B) | [Link](https://modelscope.cn/models/qwen/Qwen3-VL-Reranker-8B) |
**Install download dependencies:**
**Download from Hugging Face:**
```bash
uv pip install huggingface-hub
huggingface-cli download Qwen/Qwen3-VL-Embedding-2B --local-dir ./models/Qwen3-VL-Embedding-2B
```
**Download from ModelScope:**
```bash
uv pip install modelscope
modelscope download --model qwen/Qwen3-VL-Embedding-2B --local_dir ./models/Qwen3-VL-Embedding-2B
```
## Usage
### Quick Start
#### Embedding Model
##### Transformers usage
```python
import torch
from src.models.qwen3_vl_embedding import Qwen3VLEmbedder
model = Qwen3VLEmbedder(
model_name_or_path="./models/Qwen3-VL-Embedding-2B",
# flash_attention_2 for better acceleration and memory saving
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2"
)
inputs = [{
"text": "A woman playing with her dog on a beach at sunset.",
"instruction": "Retrieve images or text relevant to the user's query.",
}, {
"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust."
}, {
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
}, {
"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust.",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
}]
embeddings = model.process(inputs)
print(embeddings @ embeddings.T)
```
##### vLLM usage
> **Note**: Requires vLLM >= 0.14.0
For vLLM usage examples with the embedding model, please refer to [examples/embedding_vllm.ipynb](examples/embedding_vllm.ipynb).
#### Reranking Model
##### Transformers usage
```python
import torch
from src.models.qwen3_vl_reranker import Qwen3VLReranker
model = Qwen3VLReranker(
model_name_or_path="./models/Qwen3-VL-Reranker-2B",
# flash_attention_2 for better acceleration and memory saving
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2"
)
inputs = {
"instruction": "Retrieve images or text relevant to the user's query.",
"query": {"text": "A woman playing with her dog on a beach at sunset."},
"documents": [
{"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust."},
{"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
{"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust.",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}
],
"fps": 1.0,
"max_frames": 64
}
scores = model.process(inputs)
print(scores)
```
##### vLLM usage
> **Note**: Requires vLLM >= 0.14.0
For vLLM usage examples with the reranker model, please refer to [examples/reranker_vllm.ipynb](examples/reranker_vllm.ipynb).
### Model Input Specification
#### Multimodal Object
A dictionary that can contain the following keys:
- **text**: Text input as a string or a list of strings
- **image**: Image input, supports:
- Local file path
- URL (network path)
- `PIL.Image.Image` instance
- List of any combination of the above (multiple images)
- **video**: Video input, supports:
- Local file path
- URL (network path)
- Sequence of video frames (list of image paths or `PIL.Image.Image` instances)
- List of any combination of the above (multiple videos)
**Note**: All input types (text, image, video) now support both single objects and lists of objects, allowing you to provide multiple inputs of the same type in a single request. For example, you can pass multiple images as a list, multiple text strings as a list, or multiple videos as a list.
#### Instruction
Task description for relevance evaluation (default: "Represent the user's input")
#### Video Sampling Settings
Only effective when video input is a video file:
- **fps**: Frame sampling rate per second (frames per second)
- **max_frames**: Maximum number of frames to sample
#### Input Format
**Embedding Model**: A list of dictionaries, where each dictionary contains:
- Instruction (optional)
- Video sampling settings (optional)
- Multimodal object keys (text, image, and/or video)
**Reranking Model**: A dictionary containing:
- **query**: A multimodal object
- **documents**: A list of multimodal objects
- **instruction**: Task description (optional)
- **fps**: Video sampling rate (optional)
- **max_frames**: Maximum frames (optional)
### Embedding Model
#### Model Initialization Parameters
```python
Qwen3VLEmbedder(
model_name_or_path="./models/Qwen3-VL-Embedding-2B",
max_length=8192, # Default context length
min_pixels=4096, # Minimum pixels for input images
max_pixels=1843200, # Maximum pixels for input images (equivalent to 1280×1440 resolution)
total_pixels=7864320, # Maximum total pixels for input videos (multiplied by 2 in model)
# For a 16-frame video, each frame can have up to 983040 pixels (1280×768 resolution)
fps=1.0, # Default sampling frame rate for video files (frames per second)
max_frames=64, # Maximum number of frames for video input
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2"
)
```
---
## Examples
### Embedding Model
We provide comprehensive examples [here](examples/embedding.ipynb) demonstrating various tasks across different modalities:
**Text Tasks:**
- Text Classification (AG News)
- Text Question Answering (SQuAD)
- Text Retrieval (MS MARCO)
**Image Tasks:**
- Image Classification (CIFAR-10)
- Image Question Answering (VQAv2)
- Image Retrieval (MS COCO)
Examples for video and visual document tasks are presented in the appendix of [technical report](assets/qwen3vlembedding_technical_report.pdf)
We also provide an end-to-end multimodal RAG example using Qwen3-VL-Embedding, Qwen3-VL-Reranker and Qwen3-VL [here](examples/Qwen3VL_Multimodal_RAG.ipynb).
### Reranking Model
We provide comprehensive examples [here](examples/reranker.ipynb) demonstrating various tasks across different modalities:
**Text Tasks:**
- Text Retrieval (MS MARCO)
**Image Tasks:**
- Image Retrieval (MS COCO)
---
## Model Performance
### Embedding Model
#### Evaluation Results on [MMEB-V2](https://huggingface.co/spaces/TIGER-Lab/MMEB-Leaderboard)
Results on the MMEB-V2 benchmark. All models except IFM-TTE have been re-evaluated on the updated VisDoc OOD split. CLS: classification, QA: question answering, RET: retrieval, GD: grounding, MRET: moment retrieval, VDR: ViDoRe, VR: VisRAG, OOD: out-of-distribution.
| Model | Model Size | Image CLS | Image QA | Image RET | Image GD | Image Overall | Video CLS | Video QA | Video RET | Video MRET | Video Overall | VisDoc VDRv1 | VisDoc VDRv2 | VisDoc VR | VisDoc OOD | VisDoc Overall | All |
|----------------------------|---------|-------|------|------|------|-----------|------|------|------|------|------|-------|------|--------|------|------|--------|
| **# of Datasets →** | | 10 | 10 | 12 | 4 | 36 | 5 | 5 | 5 | 3 | 18 | 10 | 4 | 6 | 4 | 24 | 78 |
| VLM2Vec | 2B | 58.7 | 49.3 | 65.0 | 72.9 | 59.7 | 33.4 | 30.5 | 20.6 | 30.7 | 28.6 | 49.8 | 13.5 | 51.8 | 48.2 | 44.0 | 47.7 |
| VLM2Vec-V2 | 2B | 62.9 | 56.3 | 69.5 | 77.3 | 64.9 | 39.3 | 34.3 | 28.8 | 36.8 | 34.6 | 75.5 | 44.9 | 79.4 | 62.2 | 69.2 | 59.2 |
| GME-2B | 2B | 54.4 | 29.9 | 66.9 | 55.5 | 51.9 | 34.9 | 42.0 | 25.6 | 31.1 | 33.6 | 86.1 | 54.0 | 82.5 | 67.5 | 76.8 | 55.3 |
| GME-7B | 7B | 57.7 | 34.7 | 71.2 | 59.3 | 56.0 | 37.4 | 50.4 | 28.4 | 37.0 | 38.4 | 89.4 | 55.6 | 85.0 | 68.3 | 79.3 | 59.1 |
| Ops-MM-embedding-v1 | 8B | 69.7 | 69.6 | 73.1 | 87.2 | 72.7 | 59.7 | 62.2 | 45.7 | 43.2 | 53.8 | 80.1 | 59.6 | 79.3 | 67.8 | 74.4 | 68.9 |
| IFM-TTE | 8B | **76.7** | 78.5 | 74.6 | 89.3 | 77.9 | 60.5 | 67.9 | 51.7 | 54.9 | 59.2 | 85.2 | 71.5 | **92.7** | 53.3 | 79.5 | 74.1 |
| RzenEmbed | 8B | 70.6 | 71.7 | 78.5 | 92.1 | 75.9 | 58.8 | 63.5 | 51.0 | 45.5 | 55.7 | 89.7 | 60.7 | 88.7 | 69.9 | 81.3 | 72.9 |
| Seed-1.6-embedding-1215 | unknown | 75.0 | 74.9 | 79.3 | 89.0 | 78.0 | **85.2** | 66.7 | **59.1** | 54.8 | **67.7** | **90.0** | 60.3 | 90.0 | 70.7 | 82.2 | 76.9 |
| **Qwen3-VL-Embedding-2B** | 2B | 70.3 | 74.3 | 74.8 | 88.5 | 75.0 | 71.9 | 64.9 | 53.9 | 53.3 | 61.9 | 84.4 | 65.3 | 86.4 | 69.4 | 79.2 | 73.2 |
| **Qwen3-VL-Embedding-8B** | 8B | 74.2 | **81.1** | **80.0** | **92.2** | **80.1** | 78.4 | **71.0** | 58.7 | **56.1** | 67.1 | 87.2 | **69.9** | 88.7 | **73.3** | **82.4** | **77.8** |
#### Evaluation Results on [MMTEB](https://huggingface.co/spaces/mteb/leaderboard)
Results on the MMTEB benchmark.
| Model | Size | Mean (Task) | Mean (Type) | Bitxt Mining | Class. | Clust. | Inst. Retri. | Multi. Class. | Pair. Class. | Rerank | Retri. | STS |
|----------------------------------|:-------:|:-------------:|:-------------:|:------------:|:------:|:------:|:------------:|:-------------:|:------------:|:------:|:------:|:----:|
| NV-Embed-v2 | 7B | 56.3 | 49.6 | 57.8 | 57.3 | 40.8 | 1.0 | 18.6 | 78.9 | 63.8 | 56.7 | 71.1 |
| GritLM-7B | 7B | 60.9 | 53.7 | 70.5 | 61.8 | 49.8 | 3.5 | 22.8 | 79.9 | 63.8 | 58.3 | 73.3 |
| BGE-M3 | 0.6B | 59.6 | 52.2 | 79.1 | 60.4 | 40.9 | -3.1 | 20.1 | 80.8 | 62.8 | 54.6 | 74.1 |
| multilingual-e5-large-instruct | 0.6B | 63.2 | 55.1 | 80.1 | 64.9 | 50.8 | -0.4 | 22.9 | 80.9 | 62.6 | 57.1 | 76.8 |
| gte-Qwen2-1.5B-instruct | 1.5B | 59.5 | 52.7 | 62.5 | 58.3 | 52.1 | 0.7 | 24.0 | 81.6 | 62.6 | 60.8 | 71.6 |
| gte-Qwen2-7b-Instruct | 7B | 62.5 | 55.9 | 73.9 | 61.6 | 52.8 | 4.9 | 25.5 | 85.1 | 65.6 | 60.1 | 74.0 |
| text-embedding-3-large | - | 58.9 | 51.4 | 62.2 | 60.3 | 46.9 | -2.7 | 22.0 | 79.2 | 63.9 | 59.3 | 71.7 |
| Cohere-embed-multilingual-v3.0 | - | 61.1 | 53.2 | 70.5 | 63.0 | 46.9 | -1.9 | 22.7 | 79.9 | 64.1 | 59.2 | 74.8 |
| Gemini Embedding | - | 68.4 | 59.6 | 79.3 | 71.8 | 54.6 | 5.2 | **29.2** | 83.6 | 65.6 | 67.7 | 79.4 |
| Qwen3-Embedding-0.6B | 0.6B | 64.3 | 56.0 | 72.2 | 66.8 | 52.3 | 5.1 | 24.6 | 80.8 | 61.4 | 64.6 | 76.2 |
| Qwen3-Embedding-4B | 4B | 69.5 | 60.9 | 79.4 | 72.3 | 57.2 | **11.6** | 26.8 | 85.1 | 65.1 | 69.6 | 80.9 |
| Qwen3-Embedding-8B | 8B | **70.6** | **61.7** | **80.9** |**74.0**|**57.7**| 10.1 | 28.7 | **86.4** |**65.6**|**70.9**|**81.1**|
| Qwen3-VL-Embedding-2B | 2B | 63.9 | 55.8 | 69.5 | 65.9 | 52.5 | 3.9 | 26.1 | 78.5 | 64.8 | 67.1 | 74.3 |
| Qwen3-VL-Embedding-8B | 8B | 67.9 | 58.9 | 77.5 | 72.0 | 55.8 | 4.5 | 28.6 | 81.1 | 65.7 | 69.4 | 75.4 |
### Reranking Model
We utilize retrieval task datasets from various subtasks of [MMEB-v2](https://huggingface.co/spaces/TIGER-Lab/MMEB-Leaderboard) and [MMTEB](https://huggingface.co/spaces/mteb/leaderboard) retrieval benchmarks. For visual document retrieval, we employ [JinaVDR](https://huggingface.co/collections/jinaai/jinavdr-visual-document-retrieval) and [ViDoRe v3](https://huggingface.co/blog/QuentinJG/introducing-vidore-v3) datasets. Our results demonstrate that all Qwen3-VL-Reranker models consistently outperform the base embedding model and baseline rerankers, with the 8B variant achieving the best performance across most tasks.
| Model | Size | MMEB-v2(Retrieval) - Avg | MMEB-v2(Retrieval) - Image | MMEB-v2(Retrieval) - Video | MMEB-v2(Retrieval) - VisDoc | MMTEB(Retrieval) | JinaVDR | ViDoRe(v3) |
|-------|------|--------------------------|----------------------------|----------------------------|------------------------------|------------------|---------|------------|
| Qwen3-VL-Embedding-2B | 2B | 73.4 | 74.8 | 53.6 | 79.2 | 68.1 | 71.0 | 52.9 |
| jina-reranker-m0 | 2B | - | 68.2 | - | **85.2** | - | 82.2 | 57.8 |
| Qwen3-VL-Reranker-2B | 2B | 75.2 | 74.0 | 53.2 | 83.2 | 70.0 | 80.9 | 60.8 |
| Qwen3-VL-Reranker-8B | 8B | **79.2** | **78.2** | **61.0** | 85.8 | **74.9** | **83.6** | **66.7** |
### Reproducing Evaluation
#### Embedding Model
We provide reproducible evaluation code for **MMEB v2** benchmark, based on [VLM2Vec](https://github.com/TIGER-AI-Lab/VLM2Vec). To reproduce the results:
1. **Download evaluation data:**
```bash
bash data/evaluation/mmeb_v2/download_data.sh
```
2. **Run evaluation:**
```bash
bash scripts/evaluation/mmeb_v2/eval_embedding.sh
```
Run the script without arguments to see the required parameters. The script will evaluate tasks and collect results automatically.
#### Reranking Model
We provide reproducible evaluation code for **MMEB v2** retrieval split. To reproduce the results:
1. **Download evaluation data:**
```bash
bash data/evaluation/mmeb_v2/download_data.sh
```
2. **Run evaluation:**
```bash
bash scripts/evaluation/mmeb_v2/eval_reranker.sh
```
Run the script without arguments to see the required parameters. The script will evaluate tasks and collect results automatically.
---
## Citation
```bibtex
@article{qwen3vlembedding,
title={Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking},
author={Li, Mingxin and Zhang, Yanzhao and Long, Dingkun and Chen, Keqin and Song, Sibo and Bai, Shuai and Yang, Zhibo and Xie, Pengjun and Yang, An and Liu, Dayiheng and Zhou, Jingren and Lin, Junyang},
journal={arXiv},
year={2026}
}
```
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# 模型唯一标识
modelCode=2081
# 模型名称
modelName=Qwen3-VL-Embedding_vllm
# 模型描述
modelDescription=Qwen3-VL-Embedding 模型可生成高维向量，适用于检索、聚类等广泛场景
# 运行过程
processType=推理
# 算法类别
appCategory=文本理解
# 框架类型
frameType=vllm
# 加速卡类型
accelerateType=K100AI
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