[Bug]: Support Qwen3 Reranker

[lovetian1991]

Your current environment

The output of python collect_env.py

Your output of `python collect_env.py` here

🐛 Describe the bug

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[FreedomLiX]

error: AttributeError: 'Qwen3ForCausalLM' object has no attribute 'pooler'

[ywh-my]

The code

reranker_model_path = "Qwen3-Reranker-0.6B"
reranker_model = LLM(model=reranker_model_path, 
                                    device="auto",
                                    disable_log_stats = False,
                                    task="score")

query_sentence,target_sentenceList = reranker_list[0],reranker_list[1:] 
        
        all_scores = []

        for target_sentence in target_sentenceList:

            (output,) = self.reranker_model.score(query_sentence,
                                                    target_sentence,use_tqdm=False,)

still has the bug : "error: AttributeError: 'Qwen3ForCausalLM' object has no attribute 'pooler'" ,and the vllm == 0.9.1

[princepride]

I refer to the official example code:

import logging
from typing import Dict, Optional, List

import json
import logging

import torch

from transformers import AutoTokenizer, is_torch_npu_available
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import destroy_model_parallel
import gc
import math
from vllm.inputs.data import TokensPrompt


        
def format_instruction(instruction, query, doc):
    text = [
        {"role": "system", "content": "Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\"."},
        {"role": "user", "content": f"<Instruct>: {instruction}\n\n<Query>: {query}\n\n<Document>: {doc}"}
    ]
    return text

def process_inputs(pairs, instruction, max_length, suffix_tokens):
    messages = [format_instruction(instruction, query, doc) for query, doc in pairs]
    messages =  tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=False, enable_thinking=False
    )
    messages = [ele[:max_length] + suffix_tokens for ele in messages]
    messages = [TokensPrompt(prompt_token_ids=ele) for ele in messages]
    return messages

def compute_logits(model, messages, sampling_params, true_token, false_token):
    outputs = model.generate(messages, sampling_params, use_tqdm=False)
    scores = []
    for i in range(len(outputs)):
        final_logits = outputs[i].outputs[0].logprobs[-1]
        token_count = len(outputs[i].outputs[0].token_ids)
        if true_token not in final_logits:
            true_logit = -10
        else:
            true_logit = final_logits[true_token].logprob
        if false_token not in final_logits:
            false_logit = -10
        else:
            false_logit = final_logits[false_token].logprob
        true_score = math.exp(true_logit)
        false_score = math.exp(false_logit)
        score = true_score / (true_score + false_score)
        scores.append(score)
    return scores

number_of_gpu = torch.cuda.device_count()
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Reranker-0.6B')
model = LLM(model='Qwen/Qwen3-Reranker-0.6B', tensor_parallel_size=number_of_gpu, max_model_len=10000, enable_prefix_caching=True, gpu_memory_utilization=0.8)
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.eos_token
suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
max_length=8192
suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
true_token = tokenizer("yes", add_special_tokens=False).input_ids[0]
false_token = tokenizer("no", add_special_tokens=False).input_ids[0]
sampling_params = SamplingParams(temperature=0, 
    max_tokens=1,
    logprobs=20, 
    allowed_token_ids=[true_token, false_token],
)

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

pairs = list(zip(queries, documents))
inputs = process_inputs(pairs, task, max_length-len(suffix_tokens), suffix_tokens)
scores = compute_logits(model, inputs, sampling_params, true_token, false_token)
print('scores', scores)

destroy_model_parallel()

It can run successfully

[vaclcer]

The code

reranker_model_path = "Qwen3-Reranker-0.6B"
reranker_model = LLM(model=reranker_model_path,
device="auto",
disable_log_stats = False,
task="score")

query_sentence,target_sentenceList = reranker_list[0],reranker_list[1:]

    all_scores = []

    for target_sentence in target_sentenceList:

        (output,) = self.reranker_model.score(query_sentence,
                                                target_sentence,use_tqdm=False,)

still has the bug : "error: AttributeError: 'Qwen3ForCausalLM' object has no attribute 'pooler'" ,and the vllm == 0.9.1

exactly the same error on v0.9.1. Using vllm-openai docker image.

[dengcao]

You can use Vllm to deploy the Qwen3-Reranker large model.There is already a temporary solution:
ModelsCope：
https://www.modelscope.cn/models/dengcao/Qwen3-Reranker-8B
https://www.modelscope.cn/models/dengcao/Qwen3-Reranker-4B
https://www.modelscope.cn/models/dengcao/Qwen3-Reranker-0.6B

GitHub：
https://github.com/dengcao/Qwen3-Reranker-8B
https://github.com/dengcao/Qwen3-Reranker-4B
https://github.com/dengcao/Qwen3-Reranker-0.6B