🤗 Hugging Face Models | 🤖 ModelScope | 📑 Paper |
🖥️ UI-TARS-desktop
🏄 Midscene (Browser Automation) | 🤗 Space | 🫨 Discord
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. To use UI-TARS in web automation, you may refer to the open-source project Midscene.js.
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 or 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.
- 🚀 01.25: We updated the Cloud Deployment section in the 中文版: GUI模型部署教程 with new information related to the ModelScope platform. You can now use the ModelScope platform for deployment.
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.
- 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.
- 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.
- 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.
- Short-Term Memory: Captures task-specific context for situational awareness.
- Long-Term Memory: Retains historical interactions and knowledge for improved decision-making.
- 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.
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) | - |
We recommend using HuggingFace Inference Endpoints for fast deployment. We provide two docs for reference:
English version: GUI Model Deployment Guide
中文版: GUI模型部署教程
We follow the same way as Qwen2-VL. Check this tutorial for more details.
We recommend using vLLM for fast deployment and inference. You need to use vllm>=0.6.1.
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}
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):
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.
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 for more details), you can also use it in UI-TARS-desktop:
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:
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)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:
## 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:
## 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
"""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)
To experience UI-TARS agent in desktop, you may refer to UI-TARS-desktop. We recommend using the 7B/72B DPO model on desktop.
Midscene.js 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 for more details about setting up the model.
UI-TARS is licensed under the Apache License 2.0.
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.
If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝
@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}
}
