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mvp-lab
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mvp-lab
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Input Modalities
Output Modalities
Supported Functionality
Multimodal performance
Pure text performance
Below, we provide simple examples to show how to use Qwen3-VL with 🤖 ModelScope and 🤗 Transformers.
The code of Qwen3-VL has been in the latest Hugging Face transformers and we advise you to build from source with command:
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pip install git+https://github.com/huggingface/transformers# pip install transformers==4.57.0 # currently, V4.57.0 is not releasedHere we show a code snippet to show how to use the chat model with transformers:
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor # default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( "Qwen/Qwen3-VL-8B-Instruct", dtype="auto", device_map="auto") # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.# model = Qwen3VLForConditionalGeneration.from_pretrained(# "Qwen/Qwen3-VL-8B-Instruct",# dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# ) processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], }] # Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")inputs = inputs.to(model.device) # Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=128)generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)bash
export greedy='false'export top_p=0.8export top_k=20export temperature=0.7export repetition_penalty=1.0export presence_penalty=1.5export out_seq_length=16384bash
export greedy='false'export top_p=1.0export top_k=40export repetition_penalty=1.0export presence_penalty=2.0export temperature=1.0export out_seq_length=32768If you find our work helpful, feel free to give us a cite.
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@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}}from transformers import Qwen3VLForConditionalGeneration, AutoProcessor # default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( "Qwen/Qwen3-VL-8B-Instruct", dtype="auto", device_map="auto") # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.# model = Qwen3VLForConditionalGeneration.from_pretrained(# "Qwen/Qwen3-VL-8B-Instruct",# dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# ) processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], }] # Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")inputs = inputs.to(model.device) # Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=128)generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)export greedy='false'export top_p=0.8export top_k=20export temperature=0.7export repetition_penalty=1.0export presence_penalty=1.5export out_seq_length=16384export greedy='false'export top_p=1.0export top_k=40export repetition_penalty=1.0export presence_penalty=2.0export temperature=1.0export out_seq_length=32768@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}}