bash
Download through ModelScope (recommended for users in Mainland China)
pip install -U modelscope
modelscope download --model Qwen/Qwen3-Omni-30B-A3B-Captioner --local_dir ./Qwen3-Omni-30B-A3B-Captioner

Download through Hugging Face
pip install -U "huggingface_hub[cli]"
huggingface-cli download Qwen/Qwen3-Omni-30B-A3B-Captioner --local-dir ./Qwen3-Omni-30B-A3B-Captioner

bash
If you already have transformers installed, please uninstall it first, or create a new Python environment
pip uninstall transformers
pip install git+https://github.com/huggingface/transformers
pip install accelerate

bash
pip install qwen-omni-utils -U

bash
pip install -U flash-attn --no-build-isolation

python
import soundfile as sf

from transformers import Qwen3OmniMoeForConditionalGeneration, Qwen3OmniMoeProcessor
from qwen_omni_utils import process_mm_info

MODEL_PATH = "Qwen/Qwen3-Omni-30B-A3B-Captioner"

model = Qwen3OmniMoeForConditionalGeneration.from_pretrained(
    MODEL_PATH,
    dtype="auto",
    device_map="auto",
    attn_implementation="flash_attention_2",
)

processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "audio", "audio": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/caption2.mp3"},
        ],
    },
]

Preparation for inference
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios, _, _ = process_mm_info(conversation, use_audio_in_video=False)
inputs = processor(text=text, 
                   audio=audios,
                   return_tensors="pt", 
                   padding=True, 
                   use_audio_in_video=False)
inputs = inputs.to(model.device).to(model.dtype)

Inference: Generation of the output text and audio
text_ids, audio = model.generate(**inputs, 
                                 thinker_return_dict_in_generate=True)

text = processor.batch_decode(text_ids.sequences[:, inputs["input_ids"].shape[1] :],
                              skip_special_tokens=True,
                              clean_up_tokenization_spaces=False)
print(text)

bash
git clone -b qwen3_omni https://github.com/wangxiongts/vllm.git
cd vllm
pip install -r requirements/build.txt
pip install -r requirements/cuda.txt
export VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/a5dd03c1ebc5e4f56f3c9d3dc0436e9c582c978f/vllm-0.9.2-cp38-abi3-manylinux1_x86_64.whl
VLLM_USE_PRECOMPILED=1 pip install -e . -v --no-build-isolation
If you meet an "Undefined symbol" error while using VLLM_USE_PRECOMPILED=1, please use "pip install -e . -v" to build from source.
Install the Transformers
pip install git+https://github.com/huggingface/transformers
pip install accelerate
pip install qwen-omni-utils -U
pip install -U flash-attn --no-build-isolation

python
import os
import torch

from vllm import LLM, SamplingParams
from transformers import Qwen3OmniMoeProcessor
from qwen_omni_utils import process_mm_info

if __name__ == '__main__':
    
vLLM engine v1 not supported yet
    os.environ['VLLM_USE_V1'] = '0'

    MODEL_PATH = "Qwen/Qwen3-Omni-30B-A3B-Captioner"

    llm = LLM(
            model=MODEL_PATH, trust_remote_code=True, gpu_memory_utilization=0.95,
            tensor_parallel_size=torch.cuda.device_count(),
            limit_mm_per_prompt={'audio': 1},
            max_num_seqs=8,
            max_model_len=32768,
            seed=1234,
    )

    sampling_params = SamplingParams(
        temperature=0.6,
        top_p=0.95,
        top_k=20,
        max_tokens=16384,
    )

    processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "audio", "audio": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/caption2.mp3"}
            ], 
        }
    ]

    text = processor.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    audios, _, _ = process_mm_info(messages, use_audio_in_video=False)

    inputs = {
        'prompt': text,
        'multi_modal_data': {},
    }

    if audios is not None:
        inputs['multi_modal_data']['audio'] = audios

    outputs = llm.generate([inputs], sampling_params=sampling_params)

    print(outputs[0].outputs[0].text)

bash
Qwen3-Omni-30B-A3B-Captioner for single GPU
vllm serve Qwen/Qwen3-Omni-30B-A3B-Captioner --port 8901 --host 127.0.0.1 --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path / -tp 1
Qwen3-Omni-30B-A3B-Captioner for multi-GPU (example on 4 GPUs)
vllm serve Qwen/Qwen3-Omni-30B-A3B-Captioner --port 8901 --host 127.0.0.1 --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path / -tp 4

bash
curl http://localhost:8901/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "messages": [
    {"role": "user", "content": [
        {"type": "audio_url", "audio_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/caption2.mp3"}}
    ]}
    ]
    }'

BibTeX
@article{Qwen3-Omni,
  title={Qwen3-Omni Technical Report},
  author={Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, Yuanjun Lv, Yongqi Wang, Dake Guo, He Wang, Linhan Ma, Pei Zhang, Xinyu Zhang, Hongkun Hao, Zishan Guo, Baosong Yang, Bin Zhang, Ziyang Ma, Xipin Wei, Shuai Bai, Keqin Chen, Xuejing Liu, Peng Wang, Mingkun Yang, Dayiheng Liu, Xingzhang Ren, Bo Zheng, Rui Men, Fan Zhou, Bowen Yu, Jianxin Yang, Le Yu, Jingren Zhou, Junyang Lin},
  journal={arXiv preprint arXiv},
  year={2025}
}