metadata
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
UIPro: Unleashing Superior Interaction Capability For GUI Agents
Model Details
Model Description
- Developed by: Brave Group, CASIA
- Model type: Vision-Language Model
- Language(s) (NLP): English
- License: Apache License 2.0
- Finetuned from model: Qwen2-VL-7B-Instruct
Model Sources
HongxinLi/UIPro-7B_Stage2_Mobile is a GUI agentic model finetuned from Qwen2-VL-7B-Instruct. This model is the mobile-oriented embodiment of UIPro and capable of solving GUI agent tasks on mobile scenarios.
- Repository: https://github.com/ZJULiHongxin/UIPro
- Paper: https://arxiv.org/abs/2509.17328
Uses
Direct Use
First, ensure that the necessary dependencies are installed:
pip install transformers
pip install qwen-vl-utils
Inference code example:
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# Default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"HongxinLi/UIPro-7B_Stage2_Mobile", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("HongxinLi/UIPro-7B_Stage2_Mobile")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "./web_6f93090a-81f6-489e-bb35-1a2838b18c01.png",
},
{"type": "text", "text": """Given the Mobile UI screenshot and previous actions, please generate the next move necessary to advance towards task completion. The user's task is: {task}
Action history: {action_history}
Now, first describe the action intent and then directly plan the next action."""},
],
}
]
Citation
BibTeX:
@InProceedings{Li_2025_ICCV,
author = {Li, Hongxin and Su, Jingran and Chen, Jingfan and Ju, Zheng and Chen, Yuntao and Li, Qing and Zhang, Zhaoxiang},
title = {UIPro: Unleashing Superior Interaction Capability For GUI Agents},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1613-1623}
}

