--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal - gui library_name: transformers --- # UI-TARS-7B-SFT [UI-TARS-2B-SFT](https://huggingface.co/bytedance-research/UI-TARS-2B-SFT)  |  [UI-TARS-2B-gguf](https://huggingface.co/bytedance-research/UI-TARS-2B-gguf)  |  [UI-TARS-7B-SFT](https://huggingface.co/bytedance-research/UI-TARS-7B-SFT)  |  [UI-TARS-7B-DPO](https://huggingface.co/bytedance-research/UI-TARS-7B-DPO)  |  [UI-TARS-7B-gguf](https://huggingface.co/bytedance-research/UI-TARS-7B-gguf)  |  [UI-TARS-72B-SFT](https://huggingface.co/bytedance-research/UI-TARS-72B-SFT)  |  [UI-TARS-72B-DPO](https://huggingface.co/bytedance-research/UI-TARS-72B-DPO) This repository contains the model for the paper [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://huggingface.co/papers/2501.12326). ## Introduction UI-TARS is a next-generation native GUI agent model designed to interact seamlessly with graphical user interfaces (GUIs) using human-like perception, reasoning, and action capabilities. Unlike traditional modular frameworks, UI-TARS integrates all key components—perception, reasoning, grounding, and memory—within a single vision-language model (VLM), enabling end-to-end task automation without predefined workflows or manual rules.

Code: https://github.com/bytedance/UI-TARS ## Performance **Perception Capabilty Evaluation** | Model | VisualWebBench | WebSRC | SQAshort | |---------------------------|---------------|---------|----------| | Qwen2-VL-7B | 73.3 | 81.8 | 84.9 | | Qwen-VL-Max | 74.1 | 91.1 | 78.6 | | Gemini-1.5-Pro | 75.4 | 88.9 | 82.2 | | UIX-Qwen2-7B | 75.9 | 82.9 | 78.8 | | Claude-3.5-Sonnet | 78.2 | 90.4 | 83.1 | | GPT-4o | 78.5 | 87.7 | 82.3 | | **UI-TARS-2B** | 72.9 | 89.2 | 86.4 | | **UI-TARS-7B** | 79.7 | **93.6** | 87.7 | | **UI-TARS-72B** | **82.8** | 89.3 | **88.6** | **Grounding Capability Evaluation** - **ScreenSpot Pro** | Agent Model | Dev-Text | Dev-Icon | Dev-Avg | Creative-Text | Creative-Icon | Creative-Avg | CAD-Text | CAD-Icon | CAD-Avg | Scientific-Text | Scientific-Icon | Scientific-Avg | Office-Text | Office-Icon | Office-Avg | OS-Text | OS-Icon | OS-Avg | Avg-Text | Avg-Icon | Avg | |--------------------------|----------|----------|----------|--------------|--------------|--------------|---------|---------|---------|---------------|---------------|---------------|------------|------------|------------|--------|--------|--------|---------|---------|------| | QwenVL-7B | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7 | 0.0 | 0.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | **0.1** | | GPT-4o | 1.3 | 0.0 | 0.7 | 1.0 | 0.0 | 0.6 | 2.0 | 0.0 | 1.5 | 2.1 | 0.0 | 1.2 | 1.1 | 0.0 | 0.9 | 0.0 | 0.0 | 0.0 | 1.3 | 0.0 | **0.8** | | SeeClick | 0.6 | 0.0 | 0.3 | 1.0 | 0.0 | 0.6 | 2.5 | 0.0 | 1.9 | 3.5 | 0.0 | 2.0 | 1.1 | 0.0 | 0.9 | 2.8 | 0.0 | 1.5 | 1.8 | 0.0 | **1.1** | | Qwen2-VL-7B | 2.6 | 0.0 | 1.3 | 1.5 | 0.0 | 0.9 | 0.5 | 0.0 | 0.4 | 6.3 | 0.0 | 3.5 | 3.4 | 1.9 | 3.0 | 0.9 | 0.0 | 0.5 | 2.5 | 0.2 | **1.6** | | OS-Atlas-4B | 7.1 | 0.0 | 3.7 | 3.0 | 1.4 | 2.3 | 2.0 | 0.0 | 1.5 | 9.0 | 5.5 | 7.5 | 5.1 | 3.8 | 4.8 | 5.6 | 0.0 | 3.1 | 5.0 | 1.7 | **3.7** | | ShowUI-2B | 16.9 | 1.4 | 9.4 | 9.1 | 0.0 | 5.3 | 2.5 | 0.0 | 1.9 | 13.2 | 7.3 | 10.6 | 15.3 | 7.5 | 13.5 | 10.3 | 2.2 | 6.6 | 10.8 | 2.6 | **7.7** | | CogAgent-18B | 14.9 | 0.7 | 8.0 | 9.6 | 0.0 | 5.6 | 7.1 | 3.1 | 6.1 | 22.2 | 1.8 | 13.4 | 13.0 | 0.0 | 10.0 | 5.6 | 0.0 | 3.1 | 12.0 | 0.8 | **7.7** | | Aria-UI | 16.2 | 0.0 | 8.4 | 23.7 | 2.1 | 14.7 | 7.6 | 1.6 | 6.1 | 27.1 | 6.4 | 18.1 | 20.3 | 1.9 | 16.1 | 4.7 | 0.0 | 2.6 | 17.1 | 2.0 | **11.3** | | UGround-7B | 26.6 | 2.1 | 14.7 | 27.3 | 2.8 | 17.0 | 14.2 | 1.6 | 11.1 | 31.9 | 2.7 | 19.3 | 31.6 | 11.3 | 27.0 | 17.8 | 0.0 | 9.7 | 25.0 | 2.8 | **16.5** | | Claude Computer Use | 22.0 | 3.9 | 12.6 | 25.9 | 3.4 | 16.8 | 14.5 | 3.7 | 11.9 | 33.9 | 15.8 | 25.8 | 30.1 | 16.3 | 26.9 | 11.0 | 4.5 | 8.1 | 23.4 | 7.1 | **17.1** | | OS-Atlas-7B | 33.1 | 1.4 | 17.7 | 28.8 | 2.8 | 17.9 | 12.2 | 4.7 | 10.3 | 37.5 | 7.3 | 24.4 | 33.9 | 5.7 | 27.4 | 27.1 | 4.5 | 16.8 | 28.1 | 4.0 | **18.9** | | UGround-V1-7B | - | - | 35.5 | - | - | 27.8 | - | - | 13.5 | - | - | 38.8 | - | - | 48.8 | - | - | 26.1 | - | - | **31.1** | | **UI-TARS-2B** | 47.4 | 4.1 | 26.4 | 42.9 | 6.3 | 27.6 | 17.8 | 4.7 | 14.6 | 56.9 | 17.3 | 39.8 | 50.3 | 17.0 | 42.6 | 21.5 | 5.6 | 14.3 | 39.6 | 8.4 | **27.7** | | **UI-TARS-7B** | 58.4 | 12.4 | 36.1 | 50.0 | 9.1 | 32.8 | **20.8**| 9.4 | **18.0**| 63.9 | **31.8** | **50.0** | **63.3** | 20.8 | 53.5 | 30.8 | **16.9**| 24.5 | 47.8 | 16.2 | **35.7** | | **UI-TARS-72B** | **63.0** | **17.3** | **40.8** | **57.1** | **15.4** | **39.6** | 18.8 | **12.5**| 17.2 | **64.6** | 20.9 | 45.7 | **63.3** | **26.4** | **54.8** | **42.1**| 15.7 | **30.1**| **50.9**| **17.5**| **38.1** | - **ScreenSpot v2** | Method | Mobile-Text | Mobile-Icon/Widget | Desktop-Text | Desktop-Icon/Widget | Web-Text | Web-Icon/Widget | Avg | |--------|-------------|-------------|-------------|-------------|-------------|---------|---------| | **Agent Framework** | | | | | | | | | GPT-4o (SeeClick) | 85.2 | 58.8 | 79.9 | 37.1 | 72.7 | 30.1 | **63.6** | | GPT-4o (OS-Atlas-4B) | 95.5 | 75.8 | 79.4 | 49.3 | 90.2 | 66.5 | **79.1** | | GPT-4o (OS-Atlas-7B) | 96.2 | 83.4 | 89.7 | 69.3 | **94.0** | 79.8 | **87.1** | | **Agent Model** | | | | | | | | | SeeClick | 78.4 | 50.7 | 70.1 | 29.3 | 55.2 | 32.5 | **55.1** | | OS-Atlas-4B | 87.2 | 59.7 | 72.7 | 46.4 | 85.9 | 63.1 | **71.9** | | OS-Atlas-7B | 95.2 | 75.8 | 90.7 | 63.6 | 90.6 | 77.3 | **84.1** | | **Our Model** | | | | | | | | | **UI-TARS-2B** | 95.2 | 79.1 | 90.7 | 68.6 | 87.2 | 78.3 | **84.7** | | **UI-TARS-7B** | **96.9** | **89.1** | **95.4** | 85.0 | 93.6 | 85.2 | **91.6** | | **UI-TARS-72B** | 94.8 | 86.3 | 91.2 | **87.9** | 91.5 | **87.7** | **90.3** | **Online Agent Capability Evaluation** | Method | OSWorld (Online) | AndroidWorld (Online) | |--------|-------------------|------------------| | **Agent Framework** | | | | GPT-4o (UGround) | - | 32.8 | | GPT-4o (Aria-UI) | 15.2 | 44.8 | | GPT-4o (Aguvis-7B) | 14.8 | 37.1 | | GPT-4o (Aguvis-72B) | 17.0 | - | | GPT-4o (OS-Atlas-7B) | 14.6 | - | | **Agent Model** | | | | GPT-4o | 5.0 | 34.5 (SoM) | | Gemini-Pro-1.5 | 5.4 | 22.8 (SoM) | | Aguvis-72B | 10.3 | 26.1 | | Claude Computer-Use | 14.9 (15 steps) | 27.9 | | Claude Computer-Use | 22.0 (50 steps) | - | | **Our Model** | | | | **UI-TARS-7B-SFT** | 17.7 (15 steps) | 33.0 | | **UI-TARS-7B-DPO** | 18.7 (15 steps) | - | | **UI-TARS-72B-SFT** | 18.8 (15 steps) | **46.6** | | **UI-TARS-72B-DPO** | **22.7** (15 steps) | - | | **UI-TARS-72B-DPO** | **24.6** (50 steps) | - | ## Deployment ### Cloud Deployment We recommend using HuggingFace Inference Endpoints for fast deployment. We provide two docs for users to refer: English version: [GUI Model Deployment Guide](https://juniper-switch-f10.notion.site/GUI-Model-Deployment-Guide-17b5350241e280058e98cea60317de71) 中文版: [GUI模型部署教程](https://bytedance.sg.larkoffice.com/docx/TCcudYwyIox5vyxiSDLlgIsTgWf#U94rdCxzBoJMLex38NPlHL21gNb) ### Local Deployment [Transformers] We follow the same way as Qwen2-VL, check this [tutorial](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#using---transformers-to-chat) for more details. ### Local Deployment [vLLM] We recommend using vLLM for fast deployment and inference. You need to use `vllm>=0.6.1`. ```bash pip install -U transformers VLLM_VERSION=0.6.6 CUDA_VERSION=cu124 pip install vllm==${VLLM_VERSION} --extra-index-url https://download.pytorch.org/whl/${CUDA_VERSION} ``` #### Start an OpenAI API Service Run the command below to start an OpenAI-compatible API service: ```bash python -m vllm.entrypoints.openai.api_server --served-model-name ui-tars --model ``` Then you can use the chat API as below with the gui prompt (choose from mobile or computer) and base64-encoded local images (see [OpenAI API protocol document](https://platform.openai.com/docs/guides/vision/uploading-base-64-encoded-images) for more details): ```python import base64 from openai import OpenAI instruction = "search for today's weather" screenshot_path = "screenshot.png" client = OpenAI( base_url="http://127.0.0.1:8000/v1", api_key="empty", ) ## Below is the prompt for mobile prompt = r"""<|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. ## Output Format ```\nAction_Summary: ... Action: ...\n``` ## Action Space click(start_box='<|box_start|>(x1,y1)<|box_end|>') long_press(start_box='<|box_start|>(x1,y1)<|box_end|>', time='') type(content='') scroll(direction='down or up or right or left') open_app(app_name='') navigate_back() navigate_home() WAIT() finished() # Submit the task regardless of whether it succeeds or fails. ## Note - Use English in `Action_Summary` part. ## User Instruction """ with open(screenshot_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()).decode("utf-8") response = client.chat.completions.create( model="ui-tars", messages=[ { "role": "user", "content": [ {"type": "text", "text": prompt + instruction}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}, ], }, ], frequency_penalty=1, max_tokens=128, ) print(response.choices[0].message.content) ``` ### Prompt Templates We provide two prompt templates currently for stable running and performance, one for mobile scene and one for personal computer scene. - Prompt template for mobile: ```python ## Below is the prompt for mobile prompt = r"""<|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. ## Output Format ```\nThought: ... Action: ...\n``` ## Action Space click(start_box='<|box_start|>(x1,y1)<|box_end|>') long_press(start_box='<|box_start|>(x1,y1)<|box_end|>', time='') type(content='') scroll(direction='down or up or right or left') open_app(app_name='') navigate_back() navigate_home() WAIT() finished() # Submit the task regardless of whether it succeeds or fails. ## Note - Use English in `Action_Summary` part. ## User Instruction """ ``` - Prompt template for computer: ```python ## Below is the prompt for computer prompt = r"""<|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task. ## Output Format ```\nThought: ... Action: ...\n``` ## Action Space click(start_box='<|box_start|>(x1,y1)<|box_end|>') left_double(start_box='<|box_start|>(x1,y1)<|box_end|>') right_single(start_box='<|box_start|>(x1,y1)<|box_end|>') drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>') hotkey(key='') type(content='') #If you want to submit your input, use \"\ \" at the end of `content`. scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left') wait() #Sleep for 5s and take a screenshot to check for any changes. finished() call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help. ## Note - Use Chinese in `Thought` part. - Summarize your next action (with its target element) in one sentence in `Thought` part. ## User Instruction """ ``` ### Local Deployment [Ollama] Ollama can deploy the model via gguf format. Bugs exist for safetensors. #### Get the model in GGUF format We provide 2B and 7B model in [GGUF](https://huggingface.co/docs/hub/en/gguf) format: 2B: https://huggingface.co/bytedance-research/UI-TARS-2B-gguf 7B: https://huggingface.co/bytedance-research/UI-TARS-7B-gguf Users can convert the model into GGUF format by using the script from [llama.cpp](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py): ```bash python3 convert_hf_to_gguf.py ``` The GGUF file will be generated under the path provided. #### Deploy GGUF model We deploy the model by following Ollama [tutorial](https://github.com/ollama/ollama?tab=readme-ov-file#customize-a-model). ```bash # Create Modelfile, Windows users can just create a file named Modelfile echo "FROM ./path/to/model.gguf" > Modelfile # Create model in Ollama ollama create ui-tars -f Modelfile # Run the model ollama run ui-tars ``` Test script is same as vLLM except two changes: ```python ... client = OpenAI( base_url="http://127.0.0.1:11434/v1/", ... ) ... response = client.chat.completions.create( model="ui-tars" # the name we create via Ollama cli ... ) ``` ### Explanation of Inference Results #### Coordinate Mapping The model generates a 2D coordinate output that represents relative positions. To convert these values to image-relative coordinates, divide each component by 1000 to obtain values in the range [0,1]. The absolute coordinates required by the Action can be calculated by: - X absolute = X relative × image width - Y absolute = Y relative × image height For example, given a screen size: 1920 × 1080, and the model generates a coordinate output of (235, 512). The X absolute is `round(1920*235/1000)=451`. The Y absolute is `round(1080*512/1000)=553`. The absolute coordinate is (451, 553) ## Use in desktop and web automation To experience ui-tars agent in desktop, you may refer to [UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop). [Midscene.js](https://github.com/web-infra-dev/Midscene) is an open-source web automation SDK that has supported UI-TARS model. Developers can use javascript and natural language to control the browser. See [this guide](https://midscenejs.com/choose-a-model) for more details about setting up the model. ## License UI-TARS is licensed under the Apache License 2.0. ## Acknowledgements This project builds upon and extends the capabilities of Qwen-2-VL, a powerful vision-language model, which serves as the foundational architecture for UI-TARS. We would like to acknowledge the contributions of the developers and researchers behind Qwen-2-VL for their groundbreaking work in the field of multimodal AI and for providing a robust base for further advancements. Additionally, we thank the broader open-source community for their datasets, tools, and insights that have facilitated the development of UI-TARS. These collaborative efforts continue to push the boundaries of what GUI automation and AI-driven agents can achieve. ## Citation If you find our paper and code useful in your research, feel free to give us a cite. ```BibTeX @article{uitars2025, author = {Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, Wanjun Zhong, Kuanye Li, Jiale Yang, Yu Miao, Woyu Lin, Longxiang Liu, Xu Jiang, Qianli Ma, Jingyu Li, Xiaojun Xiao, Kai Cai, Chuang Li, Yaowei Zheng, Chaolin Jin, Chen Li, Xiao Zhou, Minchao Wang, Haoli Chen, Zhaojian Li, Haihua Yang, Haifeng Liu, Feng Lin, Tao Peng, Xin Liu, Guang Shi}, title = {UI-TARS: Pioneering Automated GUI Interaction with Native Agents}, journal = {arXiv preprint arXiv:2501.12326}, url = {https://github.com/bytedance/UI-TARS}, year = {2025} } ```