feature-extractionsentence-transformersapache-2.0

Qwen/Qwen3-Embedding-0.6B

Name: Qwen/Qwen3-Embedding-0.6B
Author: Qwen

huggingface.co/Qwen/Qwen3-Embedding-0.6B

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8,349,661Downloads

2026-04-20Updated

sentence-transformerssafetensorsqwen3text-generationtransformerssentence-similarityfeature-extractiontext-embeddings-inferencearxiv:2506.05176base_model:Qwen/Qwen3-0.6B-Basebase_model:finetune:Qwen/Qwen3-0.6B-Baselicense:apache-2.0endpoints_compatibleregion:usdeploy:sagemakerdeploy:azure

Model card

Qwen3-Embedding-0.6B

Highlights

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios.

Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.

Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.

Model Overview

Qwen3-Embedding-0.6B has the following features:

Model Type: Text Embedding
Supported Languages: 100+ Languages
Number of Parameters: 0.6B
Context Length: 32k
Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024

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

Qwen3 Embedding Series Model list

| Model Type | Models | Size | Layers | Sequence Length | Embedding Dimension | MRL Support | Instruction Aware | |------------------|----------------------|------|--------|-----------------|---------------------|-------------|----------------| | Text Embedding | Qwen3-Embedding-0.6B | 0.6B | 28 | 32K | 1024 | Yes | Yes | | Text Embedding | Qwen3-Embedding-4B | 4B | 36 | 32K | 2560 | Yes | Yes | | Text Embedding | Qwen3-Embedding-8B | 8B | 36 | 32K | 4096 | Yes | Yes | | Text Reranking | Qwen3-Reranker-0.6B | 0.6B | 28 | 32K | - | - | Yes | | Text Reranking | Qwen3-Reranker-4B | 4B | 36 | 32K | - | - | Yes | | Text Reranking | Qwen3-Reranker-8B | 8B | 36 | 32K | - | - | Yes |

Note:
- 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.

Usage

With Transformers versions earlier than 4.51.0, you may encounter the following error: ``KeyError: 'qwen3'``

Sentence Transformers Usage

```python # Requires transformers>=4.51.0 # Requires sentence-transformers>=2.7.0

from sentence_transformers import SentenceTransformer

Load the model

model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")

We recommend enabling flash_attention_2 for better acceleration and memory saving,

# together with setting padding_side to "left": # model = SentenceTransformer( # "Qwen/Qwen3-Embedding-0.6B", # model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"}, # tokenizer_kwargs={"padding_side": "left"}, # )

The queries and documents to embed

queries = [ "What is the capital of China?", "Explain gravity", ] 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.", ]

Encode the queries and documents. Note that queries benefit from using a prompt

# Here we use the prompt called "query" stored under model.prompts, but you can # also pass your own prompt via the prompt argument query_embeddings = model.encode(queries, prompt_name="query") document_embeddings = model.encode(documents)

Compute the (cosine) similarity between the query and document embeddings

similarity = model.similarity(query_embeddings, document_embeddings) print(similarity) # tensor([[0.7646, 0.1414], # [0.1355, 0.6000]]) ```

Transformers Usage

```python # Requires transformers>=4.51.0

import torch import torch.nn.functional as F

from torch import Tensor from transformers import AutoTokenizer, AutoModel

def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) if left_padding: return last_hidden_states[:, -1] else: sequence_lengths = attention_mask.sum(dim=1) - 1 batch_size = last_hidden_states.shape[0] return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]

def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery:{query}'

Each query must come with a one-sentence instruction that describes the task

task = 'Given a web search query, retrieve relevant passages that answer the query'

queries = [ get_detailed_instruct(task, 'What is the capital of China?'), get_detailed_instruct(task, 'Explain gravity') ] # No need to add instruction for retrieval documents 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." ] input_texts = queries + documents

tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B', padding_side='left') model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B')

We recommend enabling flash_attention_2 for better acceleration and memory saving.

# model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B', attn_implementation="flash_attention_2", torch_dtype=torch.float16).cuda()

max_length = 8192

Tokenize the input texts

batch_dict = tokenizer( input_texts, padding=True, truncation=True, max_length=max_length, return_tensors="pt", ) batch_dict.to(model.device) outputs = model(**batch_dict) embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

normalize embeddings

embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) print(scores.tolist()) # [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]] ```

vLLM Usage

```python # Requires vllm>=0.8.5 import torch import vllm from vllm import LLM

def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery:{query}'

Each query must come with a one-sentence instruction that describes the task

task = 'Given a web search query, retrieve relevant passages that answer the query'

model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")

outputs = model.embed(input_texts) embeddings = torch.tensor([o.outputs.embedding for o in outputs]) scores = (embeddings[:2] @ embeddings[2:].T) print(scores.tolist()) # [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]] ```

📌 Tip: We recommend that developers customize the instruct according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an instruct on the query side can lead to a drop in retrieval performance by approximately 1% to 5%.

Text Embeddings Inference (TEI) Usage

You can either run / deploy TEI on NVIDIA GPUs as:

``bash docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B --dtype float16``

Or on CPU devices as:

``bash docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B``

And then, generate the embeddings sending a HTTP POST request as:

``bash curl http://localhost:8080/embed \ -X POST \ -d '{"inputs": ["Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of China?", "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: Explain gravity"]}' \ -H "Content-Type: application/json"``

Evaluation

MTEB (Multilingual)

| Model | Size | Mean (Task) | Mean (Type) | Bitxt Mining | Class. | Clust. | Inst. Retri. | Multi. Class. | Pair. Class. | Rerank | Retri. | STS | |----------------------------------|:-------:|:-------------:|:-------------:|:--------------:|:--------:|:--------:|:--------------:|:---------------:|:--------------:|:--------:|:--------:|:------:| | NV-Embed-v2 | 7B | 56.29 | 49.58 | 57.84 | 57.29 | 40.80 | 1.04 | 18.63 | 78.94 | 63.82 | 56.72 | 71.10| | GritLM-7B | 7B | 60.92 | 53.74 | 70.53 | 61.83 | 49.75 | 3.45 | 22.77 | 79.94 | 63.78 | 58.31 | 73.33| | BGE-M3 | 0.6B | 59.56 | 52.18 | 79.11 | 60.35 | 40.88 | -3.11 | 20.1 | 80.76 | 62.79 | 54.60 | 74.12| | multilingual-e5-large-instruct | 0.6B | 63.22 | 55.08 | 80.13 | 64.94 | 50.75 | -0.40 | 22.91 | 80.86 | 62.61 | 57.12 | 76.81| | gte-Qwen2-1.5B-instruct | 1.5B | 59.45 | 52.69 | 62.51 | 58.32 | 52.05 | 0.74 | 24.02 | 81.58 | 62.58 | 60.78 | 71.61| | gte-Qwen2-7b-Instruct | 7B | 62.51 | 55.93 | 73.92 | 61.55 | 52.77 | 4.94 | 25.48 | 85.13 | 65.55 | 60.08 | 73.98| | text-embedding-3-large | - | 58.93 | 51.41 | 62.17 | 60.27 | 46.89 | -2.68 | 22.03 | 79.17 | 63.89 | 59.27 | 71.68| | Cohere-embed-multilingual-v3.0 | - | 61.12 | 53.23 | 70.50 | 62.95 | 46.89 | -1.89 | 22.74 | 79.88 | 64.07 | 59.16 | 74.80| | Gemini Embedding | - | 68.37 | 59.59 | 79.28 | 71.82 | 54.59 | 5.18 | 29.16 | 83.63 | 65.58 | 67.71 | 79.40| | Qwen3-Embedding-0.6B | 0.6B | 64.33 | 56.00 | 72.22 | 66.83 | 52.33 | 5.09 | 24.59 | 80.83 | 61.41 | 64.64 | 76.17| | Qwen3-Embedding-4B | 4B | 69.45 | 60.86 | 79.36 | 72.33 | 57.15 | 11.56 | 26.77 | 85.05 | 65.08 | 69.60 | 80.86| | Qwen3-Embedding-8B | 8B | 70.58 | 61.69 | 80.89 | 74.00 | 57.65 | 10.06 | 28.66 | 86.40 | 65.63 | 70.88 | 81.08 |

Note: For compared models, the scores are retrieved from MTEB online leaderboard on May 24th, 2025.

MTEB (Eng v2)

| MTEB English / Models | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retri. | STS | Summ. | |--------------------------------|:--------:|:------------:|:------------:|:--------:|:--------:|:-------------:|:---------:|:--------:|:-------:|:-------:| | multilingual-e5-large-instruct | 0.6B | 65.53 | 61.21 | 75.54 | 49.89 | 86.24 | 48.74 | 53.47 | 84.72 | 29.89 | | NV-Embed-v2 | 7.8B | 69.81 | 65.00 | 87.19 | 47.66 | 88.69 | 49.61 | 62.84 | 83.82 | 35.21 | | GritLM-7B | 7.2B | 67.07 | 63.22 | 81.25 | 50.82 | 87.29 | 49.59 | 54.95 | 83.03 | 35.65 | | gte-Qwen2-1.5B-instruct | 1.5B | 67.20 | 63.26 | 85.84 | 53.54 | 87.52 | 49.25 | 50.25 | 82.51 | 33.94 | | stella_en_1.5B_v5 | 1.5B | 69.43 | 65.32 | 89.38 | 57.06 | 88.02 | 50.19 | 52.42 | 83.27 | 36.91 | | gte-Qwen2-7B-instruct | 7.6B | 70.72 | 65.77 | 88.52 | 58.97 | 85.9 | 50.47 | 58.09 | 82.69 | 35.74 | | gemini-embedding-exp-03-07 | - | 73.3 | 67.67 | 90.05 | 59.39 | 87.7 | 48.59 | 64.35 | 85.29 | 38.28 | | Qwen3-Embedding-0.6B | 0.6B | 70.70 | 64.88 | 85.76 | 54.05 | 84.37 | 48.18 | 61.83 | 86.57 | 33.43 | | Qwen3-Embedding-4B | 4B | 74.60 | 68.10 | 89.84 | 57.51 | 87.01 | 50.76 | 68.46 | 88.72 | 34.39 | | Qwen3-Embedding-8B | 8B | 75.22 | 68.71 | 90.43 | 58.57 | 87.52 | 51.56 | 69.44 | 88.58 | 34.83 |

C-MTEB (MTEB Chinese)

| C-MTEB | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retr. | STS | |------------------|--------|------------|------------|--------|--------|-------------|---------|-------|-------| | multilingual-e5-large-instruct | 0.6B | 58.08 | 58.24 | 69.80 | 48.23 | 64.52 | 57.45 | 63.65 | 45.81 | | bge-multilingual-gemma2 | 9B | 67.64 | 75.31 | 59.30 | 86.67 | 68.28 | 73.73 | 55.19 | - | | gte-Qwen2-1.5B-instruct | 1.5B | 67.12 | 67.79 | 72.53 | 54.61 | 79.5 | 68.21 | 71.86 | 60.05 | | gte-Qwen2-7B-instruct | 7.6B | 71.62 | 72.19 | 75.77 | 66.06 | 81.16 | 69.24 | 75.70 | 65.20 | | ritrieve_zh_v1 | 0.3B | 72.71 | 73.85 | 76.88 | 66.5 | 85.98 | 72.86 | 76.97 | 63.92 | | Qwen3-Embedding-0.6B | 0.6B | 66.33 | 67.45 | 71.40 | 68.74 | 76.42 | 62.58 | 71.03 | 54.52 | | Qwen3-Embedding-4B | 4B | 72.27 | 73.51 | 75.46 | 77.89 | 83.34 | 66.05 | 77.03 | 61.26 | | Qwen3-Embedding-8B | 8B | 73.84 | 75.00 | 76.97 | 80.08 | 84.23 | 66.99 | 78.21 | 63.53 |

Citation

If you find our work helpful, feel free to give us a cite.

``@article{qwen3embedding, title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models}, author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren}, journal={arXiv preprint arXiv:2506.05176}, year={2025} }``

Mirrored from the Hugging Face Hub and served from the Conceptio Open Knowledge Archive. Read the original card at https://huggingface.co/Qwen/Qwen3-Embedding-0.6B.

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