Multimodal performance
Pure text performance
Quickstart
Currently, 🤗 Transformers does not support loading these weights directly. Stay tuned!
We recommend deploying the model using vLLM or SGLang, with example launch commands provided below. For details on the runtime environment and deployment, please refer to this link.
vLLM Inference
Here we provide a code snippet demonstrating how to use vLLM to run inference with Qwen3-VL locally. For more details on efficient deployment with vLLM, please refer to the community deployment guide.
import torch
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor
from vllm import LLM, SamplingParams
import os
os.environ['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
def prepare_inputs_for_vllm(messages, processor):
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs, video_kwargs = process_vision_info(
messages,
image_patch_size=processor.image_processor.patch_size,
return_video_kwargs=True,
return_video_metadata=True
)
print(f"video_kwargs: {video_kwargs}")
mm_data = {}
if image_inputs is not None:
mm_data['image'] = image_inputs
if video_inputs is not None:
mm_data['video'] = video_inputs
return {
'prompt': text,
'multi_modal_data': mm_data,
'mm_processor_kwargs': video_kwargs
}
if __name__ == '__main__':
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/receipt.png",
},
{"type": "text", "text": "Read all the text in the image."},
],
}
]
checkpoint_path = "Qwen/Qwen3-VL-2B-Thinking-FP8"
processor = AutoProcessor.from_pretrained(checkpoint_path)
inputs = [prepare_inputs_for_vllm(message, processor) for message in [messages]]
llm = LLM(
model=checkpoint_path,
trust_remote_code=True,
gpu_memory_utilization=0.70,
enforce_eager=False,
tensor_parallel_size=torch.cuda.device_count(),
seed=0
)
sampling_params = SamplingParams(
temperature=0,
max_tokens=1024,
top_k=-1,
stop_token_ids=[],
)
for i, input_ in enumerate(inputs):
print()
print('=' * 40)
print(f"Inputs[{i}]: {input_['prompt']=!r}")
print('\n' + '>' * 40)
outputs = llm.generate(inputs, sampling_params=sampling_params)
for i, output in enumerate(outputs):
generated_text = output.outputs[0].text
print()
print('=' * 40)
print(f"Generated text: {generated_text!r}")
SGLang Inference
Here we provide a code snippet demonstrating how to use SGLang to run inference with Qwen3-VL locally.
import time
from PIL import Image
from sglang import Engine
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor, AutoConfig
if __name__ == "__main__":
checkpoint_path = "Qwen/Qwen3-VL-2B-Thinking-FP8"
processor = AutoProcessor.from_pretrained(checkpoint_path)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/receipt.png",
},
{"type": "text", "text": "Read all the text in the image."},
],
}
]
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
image_inputs, _ = process_vision_info(messages, image_patch_size=processor.image_processor.patch_size)
llm = Engine(
model_path=checkpoint_path,
enable_multimodal=True,
mem_fraction_static=0.8,
tp_size=torch.cuda.device_count(),
attention_backend="fa3"
)
start = time.time()
sampling_params = {"max_new_tokens": 1024}
response = llm.generate(prompt=text, image_data=image_inputs, sampling_params=sampling_params)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response['text']}")
Generation Hyperparameters
VL
export greedy='false'
export top_p=0.95
export top_k=20
export repetition_penalty=1.0
export presence_penalty=0.0
export temperature=1.0
export out_seq_length=40960
Text
export greedy='false'
export top_p=0.95
export top_k=20
export repetition_penalty=1.0
export presence_penalty=1.5
export temperature=1.0
export out_seq_length=32768 (for aime, lcb, and gpqa, it is recommended to set to 81920)
Citation
If you find our work helpful, feel free to give us a cite.
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
@article{Qwen2.5-VL,
title={Qwen2.5-VL Technical Report},
author={Bai, Shuai and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Song, Sibo and Dang, Kai and Wang, Peng and Wang, Shijie and Tang, Jun and Zhong, Humen and Zhu, Yuanzhi and Yang, Mingkun and Li, Zhaohai and Wan, Jianqiang and Wang, Pengfei and Ding, Wei and Fu, Zheren and Xu, Yiheng and Ye, Jiabo and Zhang, Xi and Xie, Tianbao and Cheng, Zesen and Zhang, Hang and Yang, Zhibo and Xu, Haiyang and Lin, Junyang},
journal={arXiv preprint arXiv:2502.13923},
year={2025}
}
@article{Qwen2VL,
title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
journal={arXiv preprint arXiv:2409.12191},
year={2024}
}
@article{Qwen-VL,
title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
journal={arXiv preprint arXiv:2308.12966},
year={2023}
}
