Name: Qwen3 VL Embedding 8B
Author: Qwen

Overview

Qwen3 VL Embedding 8B is a high-capacity multimodal embedding model designed to map both visual and textual data into a shared vector space. Unlike standard text-only models, this 8B parameter architecture is optimized for cross-modal retrieval and semantic similarity tasks, making it an ideal backbone for advanced RAG (Retrieval-Augmented Generation) pipelines that handle images and documents. Developers can leverage it to build efficient visual search engines, automated image tagging systems, or complex recommendation engines where visual context is critical. Its Apache-2.0 license ensures flexibility for commercial deployment, while the model's scale provides a significant boost in nuance and accuracy over smaller embedding models, reducing the need for extensive fine-tuning on domain-specific datasets.

Highlights

Unified vector space for text and image embeddings
Optimized for high-accuracy cross-modal semantic retrieval
Apache-2.0 license allows for flexible commercial integration
8B parameter scale improves nuance in complex queries
Ideal for multimodal RAG and visual search pipelines

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Qwen/Qwen3-VL-Embedding-8B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-VL-Embedding-8B")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download Qwen/Qwen3-VL-Embedding-8B

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download Qwen/Qwen3-VL-Embedding-8B config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Qwen/Qwen3-VL-Embedding-8B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen3-VL-Embedding-8B')
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-VL-Embedding-8B')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model Qwen/Qwen3-VL-Embedding-8B

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model Qwen/Qwen3-VL-Embedding-8B README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Qwen/Qwen3-VL-Embedding-8B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/Qwen/Qwen3-VL-Embedding-8B.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Qwen/Qwen3-VL-Embedding-8B.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Qwen/Qwen3-VL-Embedding-8B')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: sentence-similarity

base_model:

Qwen/Qwen3-VL-8B-Instruct

tags:

sentence-transformers

transformers

multimodal embedding

qwen

embedding

---

Qwen3-VL-Embedding-8B

Highlights

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.

While the Embedding model generates high-dimensional vectors for broad applications like retrieval and clustering, the Reranker model is engineered to refine these results, establishing a comprehensive pipeline for state-of-the-art multimodal search.

Multimodal Versatility: Both models seamlessly handle a wide range of inputs—including text, images, screenshots, and video—within a unified framework. They deliver state-of-the-art performance across diverse multimodal tasks such as image-text retrieval, video-text matching, visual question answering (VQA), and multimodal content clustering.

Unified Representation Learning (Embedding): By leveraging the Qwen3-VL architecture, the Embedding model generates semantically rich vectors that capture both visual and textual information in a shared space. This facilitates efficient similarity computation and retrieval across different modalities.

High-Precision Reranking (Reranker): We also introduce the Qwen3-VL-Reranker series to complement the embedding model. The reranker takes a (query, document) pair as input—where both query and document may contain arbitrary single or mixed modalities—and outputs a precise relevance score. In retrieval pipelines, the two models are typically used in tandem: the embedding model performs efficient initial recall, while the reranker refines results in a subsequent re-ranking stage. This two-stage approach significantly boosts retrieval accuracy.

Exceptional Practicality: Inheriting Qwen3-VL’s multilingual capabilities, the series supports over 30 languages, making it ideal for global applications. It is highly practical for real-world scenarios, offering flexible vector dimensions, customizable instructions for specific use cases, and strong performance even with quantized embeddings. These capabilities enable developers to seamlessly integrate both models into existing pipelines, unlocking powerful cross-lingual and cross-modal understanding.

Model Overview

Qwen3-VL-Embedding-8B has the following features:

Model Type: MultiModal Embedding

Supported Languages: 30+ Languages

Supported Input Modalities: Text, images, screenshots, videos, and arbitrary multimodal combinations (e.g., text + image, text + video)

Number of Parameters: 8B

Context Length: 32k

Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 64 to 4096

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our technical report, blog, GitHub.

Qwen3-VL-Embedding and Qwen3-VL-Reranker Model list

| Model | Size | Model Layers | Sequence Length | Embedding Dimension | Quantization Support | MRL Support | Instruction Aware |
|---|---|---|---|---|----------------------|---|---|
| Qwen3-VL-Embedding-2B | 2B | 28 | 32K | 2048 | Yes | Yes | Yes |
| Qwen3-VL-Embedding-8B | 8B | 36 | 32K | 4096 | Yes | Yes | Yes |
| Qwen3-VL-Reranker-2B | 2B | 28 | 32K | - | - | - | Yes |
| Qwen3-VL-Reranker-8B | 8B | 36 | 32K | - | - | - | Yes |

> Note:
> - Quantization Support indicates the supported quantization post process for the output embedding.
> - MRL Support indicates whether the embedding model supports custom dimensions for the final embedding.
> - Instruction Aware notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
> Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.

Model Performance

Evaluation Results on MMEB-V2

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 | 70.2 | 74.4 | 74.9 | 88.6 | 75.0 | 72.8 | 63.8 | 52.3 | 51.6 | 61.1 | 85.2 | 66.0 | 86.3 | 74.3 | 80.2 | 73.4 |
| Qwen3-VL-Embedding-8B | 8B | 74.4 | 81.0 | 80.0 | 92.2 | 80.1 | 79.1 | 70.1 | 57.0 | 53.2 | 66.1 | 88.2 | 69.9 | 88.8 | 78.3 | 83.3 | 77.9 |

Evaluation Results on MMTEB

Results on the MMTEB benchmark.

| Model | Size | Mean (Task) | Mean (Type) | Bitxt Mining | Class. | Clust. | Inst. Retri. | Multi. Class. | Pair. Class. | Rerank | Retri. | STS |
|----------------------------------|:-------:|:-------------:|:-------------:|:--------------:|:--------:|:--------:|:--------------:|:---------------:|:------