README_v1.md

<!-- <p align="center">
  <img alt="UI-TARS"  width="260" src="figures/icon.png">
</p>

# UI-TARS: Pioneering Automated GUI Interaction with Native Agents -->
![Local Image](figures/writer.png)
<p align="center">
        🤗 <a href="https://huggingface.co/bytedance-research/UI-TARS-7B-DPO">Hugging Face Models</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://www.modelscope.cn/models/bytedance-research/UI-TARS-7B-DPO">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2501.12326">Paper</a> &nbsp&nbsp  |&nbsp&nbsp</a>
🖥️ <a href="https://github.com/bytedance/UI-TARS-desktop">UI-TARS-desktop</a>&nbsp&nbsp  <br>🏄 <a href="https://github.com/web-infra-dev/Midscene">Midscene (Browser Automation) </a>&nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/spaces/bytedance-research/UI-TARS">Space</a>&nbsp&nbsp | &nbsp&nbsp🫨 <a href="https://discord.gg/pTXwYVjfcs">Discord</a>&nbsp&nbsp
</p>

We also offer a **UI-TARS-desktop** version, which can operate on your **local personal device**. To use it, please visit [https://github.com/bytedance/UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop). To use UI-TARS in web automation, you may refer to the open-source project [Midscene.js](https://github.com/web-infra-dev/Midscene).
### ⚠️ Important Announcement: GGUF Model Performance

The **GGUF model** has undergone quantization, but unfortunately, its performance cannot be guaranteed. As a result, we have decided to **downgrade** it. 

💡 **Alternative Solution**:  
You can use **[Cloud Deployment](#cloud-deployment)** or **[Local Deployment [vLLM]](#local-deployment-vllm)**(If you have enough GPU resources) instead. 

We appreciate your understanding and patience as we work to ensure the best possible experience.

## Updates
- ✨ We updated the OSWorld inference scripts from the original official [OSWorld repository](https://github.com/xlang-ai/OSWorld/blob/main/run_uitars.py). Now, you can use the OSWorld official inference scripts for deployment and we've provided [trajectory examples](https://drive.google.com/file/d/1N9dYzAB9xSiHwE9VSdEi9xSpB9eXfVZT/view?usp=sharing) for OSWorld to help you get started.
- 🚀 01.25: We updated the **[Cloud Deployment](#cloud-deployment)** section in the 中文版: [GUI模型部署教程](https://bytedance.sg.larkoffice.com/docx/TCcudYwyIox5vyxiSDLlgIsTgWf#U94rdCxzBoJMLex38NPlHL21gNb) with new information related to the ModelScope platform. You can now use the ModelScope platform for deployment.



## Overview
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.
![Local Image](figures/UI-TARS-vs-Previous-SOTA.png)
![Local Image](figures/UI-TARS.png)


## Core Features
### Perception
- **Comprehensive GUI Understanding**: Processes multimodal inputs (text, images, interactions) to build a coherent understanding of interfaces.
- **Real-Time Interaction**: Continuously monitors dynamic GUIs and responds accurately to changes in real-time.

### Action
- **Unified Action Space**: Standardized action definitions across platforms (desktop, mobile, and web).
- **Platform-Specific Actions**: Supports additional actions like hotkeys, long press, and platform-specific gestures.

### Reasoning
- **System 1 & System 2 Reasoning**: Combines fast, intuitive responses with deliberate, high-level planning for complex tasks.
- **Task Decomposition & Reflection**: Supports multi-step planning, reflection, and error correction for robust task execution.

### Memory
- **Short-Term Memory**: Captures task-specific context for situational awareness.
- **Long-Term Memory**: Retains historical interactions and knowledge for improved decision-making.

## Capabilities
- **Cross-Platform Interaction**: Supports desktop, mobile, and web environments with a unified action framework.
- **Multi-Step Task Execution**: Trained to handle complex tasks through multi-step trajectories and reasoning.
- **Learning from Synthetic and Real Data**: Combines large-scale annotated and synthetic datasets for improved generalization and robustness.

## 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**

| Method |  Mobile-Text | Mobile-Icon/Widget | Desktop-Text | Desktop-Icon/Widget | Web-Text | Web-Icon/Widget | Avg |
|--------|-------------|-------------|-------------|-------------|-------------|---------|---------|
| **Agent Framework**  | | | | | | | |
| GPT-4 (SeeClick) |  76.6 | 55.5 | 68.0 | 28.6 | 40.9 | 23.3 | **48.8** |
| GPT-4 (OmniParser)  | 93.9 | 57.0 | 91.3 | 63.6 | 81.3 | 51.0 | **73.0** |
| GPT-4 (UGround-7B)  | 90.1 | 70.3 | 87.1 | 55.7 | 85.7 | 64.6 | **75.6** |
| GPT-4o (SeeClick)  | 81.0 | 59.8 | 69.6 | 33.6 | 43.9 | 26.2 | **52.3** |
| GPT-4o (UGround-7B)  | 93.4 | 76.9 | 92.8 | 67.9 | 88.7 | 68.9 | **81.4** |
| **Agent Model**   | | | | | | | |
| GPT-4  | 22.6 | 24.5 | 20.2 | 11.8 | 9.2 | 8.8 | **16.2** |
| GPT-4o  | 20.2 | 24.9 | 21.1 | 23.6 | 12.2 | 7.8 | **18.3** |
| CogAgent  | 67.0 | 24.0 | 74.2 | 20.0 | 70.4 | 28.6 | **47.4** |
| SeeClick  | 78.0 | 52.0 | 72.2 | 30.0 | 55.7 | 32.5 | **53.4** |
| Qwen2-VL  | 75.5 | 60.7 | 76.3 | 54.3 | 35.2 | 25.7 | **55.3** |
| UGround-7B  | 82.8 | 60.3 | 82.5 | 63.6 | 80.4 | 70.4 | **73.3** |
| Aguvis-G-7B  | 88.3 | 78.2 | 88.1 | 70.7 | 85.7 | 74.8 | **81.8** |
| OS-Atlas-7B | 93.0 | 72.9 | 91.8 | 62.9 | 90.9 | 74.3 | **82.5** |
| Claude Computer Use  | - | - | - | - | - | - | **83.0** |
| Gemini 2.0 (Project Mariner)  | - | - | - | - | - | - | **84.0** |
| Aguvis-7B  | **95.6** | 77.7 | 93.8 | 67.1 | 88.3 | 75.2 | **84.4** |
| Aguvis-72B  | 94.5 | **85.2** | 95.4 | 77.9 | **91.3** | **85.9** | **89.2** |
| **Our Model**   | | | | | | | |
| **UI-TARS-2B**  | 93.0 | 75.5 | 90.7 | 68.6 | 84.3 | 74.8 | **82.3** |
| **UI-TARS-7B**  | 94.5 | **85.2** | **95.9** | 85.7 | 90.0 | 83.5 | **89.5** |
| **UI-TARS-72B**  | 94.9 | 82.5 | 89.7 | **88.6** | 88.7 | 85.0 | **88.4** |


- **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** |


**Offline Agent Capability Evaluation**
- **Multimodal Mind2Web**

| Method |  Cross-Task Ele.Acc | Cross-Task Op.F1 | Cross-Task Step SR | Cross-Website Ele.Acc | Cross-Website Op.F1 | Cross-Website Step SR | Cross-Domain Ele.Acc | Cross-Domain Op.F1 | Cross-Domain Step SR |
|--------|----------------------|-------------------|--------------------|----------------------|--------------------|-------------------|--------------------|-------------------|-------------------|
| **Agent Framework**  | | | | | | | | | |
| GPT-4o (SeeClick)  | 32.1 | - | - | 33.1 | - | - | 33.5 | - | - |
| GPT-4o (UGround)  | 47.7 | - | - | 46.0 | - | - | 46.6 | - | - |
| GPT-4o (Aria-UI)  | 57.6 | - | - | 57.7 | - | - | 61.4 | - | - |
| GPT-4V (OmniParser)  | 42.4 | 87.6 | 39.4 | 41.0 | 84.8 | 36.5 | 45.5 | 85.7 | 42.0 |
| **Agent Model** |  | | | | | | | | |
| GPT-4o  | 5.7 | 77.2 | 4.3 | 5.7 | 79.0 | 3.9 | 5.5 | 86.4 | 4.5 |
| GPT-4 (SOM)  | 29.6 | - | 20.3 | 20.1 | - | 13.9 | 27.0 | - | 23.7 |
| GPT-3.5 (Text-only)  | 19.4 | 59.2 | 16.8 | 14.9 | 56.5 | 14.1 | 25.2 | 57.9 | 24.1 |
| GPT-4 (Text-only)  | 40.8 | 63.1 | 32.3 | 30.2 | 61.0 | 27.0 | 35.4 | 61.9 | 29.7 |
| Claude  | 62.7 | 84.7 | 53.5 | 59.5 | 79.6 | 47.7 | 64.5 | 85.4 | 56.4 |
| Aguvis-7B  | 64.2 | 89.8 | 60.4 | 60.7 | 88.1 | 54.6 | 60.4 | 89.2 | 56.6 |
| CogAgent  | - | - | 62.3 | - | - | 54.0 | - | - | 59.4 |
| Aguvis-72B  | 69.5 | 90.8 | 64.0 | 62.6 | 88.6 | 56.5 | 63.5 | 88.5 | 58.2 |
| **Our Model**  | | | | | | | | | |
| **UI-TARS-2B**  | 62.3 | 90.0 | 56.3 | 58.5 | 87.2 | 50.8 | 58.8 | 89.6 | 52.3 |
| **UI-TARS-7B**  | 73.1 | 92.2 | 67.1 | 68.2 | 90.9 | 61.7 | 66.6 | 90.9 | 60.5 |
| **UI-TARS-72B**  | **74.7** | **92.5** | **68.6** | **72.4** | **91.2** | **63.5** | **68.9** | **91.8** | **62.1** |


- **Android Control and GUI Odyssey**

| Agent Models        | AndroidControl-Low Type | AndroidControl-Low Grounding | AndroidControl-Low SR | AndroidControl-High Type | AndroidControl-High Grounding | AndroidControl-High SR | GUIOdyssey Type | GUIOdyssey Grounding | GUIOdyssey SR |
|---------------------|----------------------|----------------------|----------------|----------------------|----------------------|----------------|----------------|----------------|----------------|
| Claude             | 74.3                 | 0.0                  | 19.4           | 63.7                 | 0.0                  | 12.5           | 60.9           | 0.0            | 3.1            |
| GPT-4o             | 74.3                 | 0.0                  | 19.4           | 66.3                 | 0.0                  | 20.8           | 34.3           | 0.0            | 3.3            |
| SeeClick           | 93.0                 | 73.4                 | 75.0           | 82.9                 | 62.9                 | 59.1           | 71.0           | 52.4           | 53.9           |
| InternVL-2-4B      | 90.9                 | 84.1                 | 80.1           | 84.1                 | 72.7                 | 66.7           | 82.1           | 55.5           | 51.5           |
| Qwen2-VL-7B       | 91.9                 | 86.5                 | 82.6           | 83.8                 | 77.7                 | 69.7           | 83.5           | 65.9           | 60.2           |
| Aria-UI           | --                   | 87.7                 | 67.3           | --                   | 43.2                 | 10.2           | --             | 86.8           | 36.5           |
| OS-Atlas-4B       | 91.9                 | 83.8                 | 80.6           | 84.7                 | 73.8                 | 67.5           | 83.5           | 61.4           | 56.4           |
| OS-Atlas-7B       | 93.6                 | 88.0                 | 85.2           | 85.2                 | 78.5                 | 71.2           | 84.5           | 67.8           | 62.0           |
| Aguvis-7B         | --                   | --                   | 80.5           | --                   | --                   | 61.5           | --             | --             | --             |
| Aguvis-72B        | --                   | --                   | 84.4           | --                   | --                   | 66.4           | --             | --             | --             |
| **UI-TARS-2B**   | **98.1**             | 87.3                 | 89.3           | 81.2                 | 78.4                 | 68.9           | 93.9           | 86.8           | 83.4           |
| **UI-TARS-7B**   | 98.0                 | 89.3                 | 90.8           | 83.7                 | 80.5                 | 72.5           | 94.6           | 90.1           | 87.0           |
| **UI-TARS-72B**  | **98.1**             | **89.9**             | **91.3**       | **85.2**             | **81.5**             | **74.7**       | **95.4**       | **91.4**       | **88.6**       |

**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 reference:

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}

```
#### Download the Model
We provide three model sizes on Hugging Face: **2B**, **7B**, and **72B**. To achieve the best performance, we recommend using the **7B-DPO** or **72B-DPO** model (depends on your GPU configuration):

- [2B-SFT](https://huggingface.co/bytedance-research/UI-TARS-2B-SFT)
- [7B-SFT](https://huggingface.co/bytedance-research/UI-TARS-7B-SFT)
- [7B-DPO](https://huggingface.co/bytedance-research/UI-TARS-7B-DPO)
- [72B-SFT](https://huggingface.co/bytedance-research/UI-TARS-72B-SFT)
- [72B-DPO](https://huggingface.co/bytedance-research/UI-TARS-72B-DPO)


#### Start an OpenAI API Service
Run the command below to start an OpenAI-compatible API service. It is recommended to set the tensor parallel size `-tp=1` for 7B models and `-tp=4` for 72B models.

```bash
python -m vllm.entrypoints.openai.api_server --served-model-name ui-tars \
    --model <path to your model> --limit-mm-per-prompt image=5 -tp <tp>
```

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), you can also use it in [UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop):
```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"""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
"""

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)
```

For single step grounding task or inference on grounding dataset such as Seeclick, kindly refer to the following script:
```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"""Output only the coordinate of one point in your response. What element matches the following task: """

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": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}},
                {"type": "text", "text": prompt + instruction}
            ],
        },
    ],
    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"""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(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>')
press_home()
press_back()
finished(content='') # Submit the task regardless of whether it succeeds or fails.

## Note
- Use English in `Thought` part.

- Write a small plan and finally summarize your next action (with its target element) in one sentence in `Thought` part.

## User Instruction
"""
```

- Prompt template for computer:
```python
## Below is the prompt for computer
prompt = r"""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. -->Ollama will be coming soon. Please be patient and wait~ 😊
<!-- #### 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 <path to your model>
```

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). We recommend using the **7B/72B DPO model** on 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 Qwen2-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 Qwen2-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, please consider giving a star :star: and citation :pencil:

```BibTeX
@article{qin2025ui,
  title={UI-TARS: Pioneering Automated GUI Interaction with Native Agents},
  author={Qin, Yujia and Ye, Yining and Fang, Junjie and Wang, Haoming and Liang, Shihao and Tian, Shizuo and Zhang, Junda and Li, Jiahao and Li, Yunxin and Huang, Shijue and others},
  journal={arXiv preprint arXiv:2501.12326},
  year={2025}
}
```