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Qwen3 Embedding

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 capabilities of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.

Qwen3 Embedding Series Model list

Note: - MRL (Matryoshka Representation Learning) 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.

Multilingual Support

Qwen3-Embedding model series shares the multilingual support capabilities of the Qwen3 base model.

Click to expand the list of supported languages

Usage

With Transformers versions earlier than 4.51.0, you may encounter the following error:

KeyError: 'qwen3'

Embedding Model

Transformers Usage

# 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)
with torch.no_grad():
    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

# 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'

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

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]]

Sentence Transformers Usage

# 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.",
]

with torch.no_grad():
    # 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]])

Reranker Model

Transformers Usage

```python

Requires transformers>=4.51.0

import torch from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM

def format_instruction(instruction, query, doc): if instruction is None: instruction = 'Given a web search query, retrieve relevant passages that answer the query' output = ": {instruction}\n: {query}\n: {doc}".format(instruction=instruction,query=query, doc=doc) return output

def process_inputs(pairs): inputs = tokenizer( pairs, padding=False, truncation='longest_first', return_attention_mask=False, max_length=max_length - len(prefix_tokens) - len(suffix_tokens) ) for i, ele in enumerate(inputs['input_ids']): inputs['input_ids'][i] = prefix_tokens + ele + suffix_tokens inputs = tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_length) for key in inputs: inputs[key] = inputs[key].to(model.device) return inputs

@torch.no_grad() def compute_logits(inputs, kwargs): batch_scores = model(inputs).logits[:, -1, :] true_vector = batch_scores[:, token_true_id] false_vector = batch_scores[:, token_false_id] batch_scores = torch.stack([false_vector, true_vector], dim=1) batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1) scores = batch_scores[:, 1].exp().tolist() return scores

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Reranker-0.6B", padding_side='left') model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-0.6B").eval()

We recommend enabling flash_attention_2 for better acceleration and memory saving.

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-0.6B", torch_dtype=torch.float16, attn_implementation="flash_attention_2").cuda().eval()

token_false_id = tokenizer.convert_tokens_to_ids("no") token_true_id = tokenizer.convert_tokens_to_ids("yes") max_length = 8192

prefix = "<|im_start|>system\nJudge 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|>\n<|im_start|>user\n" suffix = "<|im_end|>\n<|im_start|>assistant\n\n\n\n\n" prefix_tokens = tokenizer.encode(prefix, add_special_tokens=False) suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)

task = 'Given a