README.md

---
license: other
language:
- en
- zh
metrics:
- accuracy
base_model:
- Qwen/Qwen3-8B-Base
- tencent/POINTS-Reader
- WePOINTS/POINTS-1-5-Qwen-2-5-7B-Chat
tags:
- GUI
- GUI-Grounding
- Vision-language
- multimodal
---

<p align="center">
    <img src="images/logo.png"/>
<p>

<p align="center">
  <a href="https://huggingface.co/tencent/POINTS-GUI-G">
    <img src="https://img.shields.io/badge/%F0%9F%A4%97_HuggingFace-Model-ffbd45.svg" alt="HuggingFace">
  </a>
  <a href="https://github.com/Tencent/POINTS-GUI">
    <img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub Code">
  </a>
  <a href="https://huggingface.co/papers/2602.06391">
    <img src="https://img.shields.io/badge/Paper-POINTS--GUI--G-d4333f?logo=arxiv&logoColor=white&colorA=cccccc&colorB=d4333f&style=flat" alt="Paper">
  </a>
  <a href="https://komarev.com/ghpvc/?username=tencent&repo=POINTS-GUI&color=brightgreen&label=Views" alt="view">
    <img src="https://komarev.com/ghpvc/?username=tencent&repo=POINTS-GUI&color=brightgreen&label=Views" alt="view">
  </a>
</p>

## News

- 🔜 <b>Upcoming:</b> The <b>End-to-End GUI Agent Model</b> is currently under active development and will be released in a subsequent update. Stay tuned!
- 🚀 2026.02.06: We are happy to present <b>POINTS-GUI-G</b>, our specialized GUI Grounding Model. To facilitate reproducible evaluation, we provide comprehensive scripts and guidelines in our <a href="https://github.com/Tencent/POINTS-GUI/tree/main/evaluation">GitHub Repository</a>.

## Introduction

1. **State-of-the-Art Performance**: POINTS-GUI-G-8B achieves leading results on multiple GUI grounding benchmarks, with 59.9 on ScreenSpot-Pro, 66.0 on OSWorld-G, 95.7 on ScreenSpot-v2, and 49.9 on UI-Vision.

2. **Full-Stack Mastery**: Unlike many current GUI agents that build upon models already possessing strong grounding capabilities (e.g., Qwen3-VL), POINTS-GUI-G-8B is developed from the ground up using POINTS-1.5 (which initially lacked native grounding ability). We have mastered the complete technical pipeline, proving that a specialized GUI specialist can be built from a general-purpose base model through targeted optimization.

3. **Refined Data Engineering**: Existing GUI datasets differ in coordinate systems, task formats, and contain substantial noise. We build a unified data pipeline that (1) standardizes all coordinates to a [0, 1] range and reformats heterogeneous tasks into a single “locate UI element” formulation, (2) automatically filters noisy or incorrect annotations, and (3) explicitly increases difficulty via layout-based filtering and synthetic hard cases

## Results

We evaluate POINTS-GUI-G-8B on four widely used GUI grounding benchmarks: ScreenSpot-v2, ScreenSpot-Pro, OSWorld-G, and UI-Vision. The figure below summarizes our results compared with existing open-source and proprietary baselines.

![Example 1](images/results.png)

## Examples

### Prediction on desktop screenshots

![Example 1](images/example_desktop_1.png)
![Example 1](images/example_desktop_2.png)
![Example 1](images/example_desktop_3.png)

### Prediction on mobile screenshots

![Example 1](images/example_mobile.png)

### Prediction on web screenshots

![Example 1](images/example_web_1.png)
![Example 1](images/example_web_2.png)
![Example 1](images/example_web_3.png)

## Getting Started

This following code snippet has been tested with following environment:

```
python==3.12.11
torch==2.9.1
transformers==4.57.1
cuda==12.6
```

### Run with Transformers

Please first install [WePOINTS](https://github.com/WePOINTS/WePOINTS) using the following command:

```sh
git clone https://github.com/WePOINTS/WePOINTS.git
cd ./WePOINTS
pip install -e .
```

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, Qwen2VLImageProcessor
import torch

system_prompt_point = (
    'You are a GUI agent. Based on the UI screenshot provided, please locate the exact position of the element that matches the instruction given by the user.\n\n'
    'Requirements for the output:\n'
    '- Return only the point (x, y) representing the center of the target element\n'
    '- Coordinates must be normalized to the range [0, 1]\n'
    '- Round each coordinate to three decimal places\n'
    '- Format the output as strictly (x, y) without any additional text\n'
)
system_prompt_bbox = (
    'You are a GUI agent. Based on the UI screenshot provided, please output the bounding box of the element that matches the instruction given by the user.\n\n'
    'Requirements for the output:\n'
    '- Return only the bounding box coordinates (x0, y0, x1, y1)\n'
    '- Coordinates must be normalized to the range [0, 1]\n'
    '- Round each coordinate to three decimal places\n'
    '- Format the output as strictly (x0, y0, x1, y1) without any additional text.\n'
)
system_prompt = system_prompt_point  # system_prompt_bbox
user_prompt = None  # replace with your instruction (e.g., 'close the window')
image_path = '/path/to/your/local/image'
model_path = 'tencent/POINTS-GUI-G'
model = AutoModelForCausalLM.from_pretrained(model_path,
                                             trust_remote_code=True,
                                             dtype=torch.bfloat16,
                                             device_map='cuda')
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
image_processor = Qwen2VLImageProcessor.from_pretrained(model_path)
content = [
            dict(type='image', image=image_path),
            dict(type='text', text=user_prompt)
          ]
messages = [
        {
            'role': 'system',
            'content': [dict(type='text', text=system_prompt)]
        },
        {
            'role': 'user',
            'content': content
        }
    ]
generation_config = {
        'max_new_tokens': 2048,
        'do_sample': False
    }
response = model.chat(
    messages,
    tokenizer,
    image_processor,
    generation_config
)
print(response)
```

### Deploy with SGLang

We have created a [Pull Request](https://github.com/sgl-project/sglang/pull/17989) for SGLang. You can check out this branch and install SGLang in editable mode by following the [official guide](https://docs.sglang.ai/get_started/install.html) prior to the merging of this PR.

#### How to Deploy

You can deploy POINTS-GUI-G with SGLang using the following command:

```
python3 -m sglang.launch_server \
--model-path tencent/POINTS-GUI-G \
--tp-size 1 \
--dp-size 1 \
--chunked-prefill-size -1 \
--mem-fraction-static 0.7 \
--chat-template qwen2-vl \
--trust-remote-code \
--port 8081
```

#### How to Use

You can use the following code to obtain results from SGLang:

```python

from typing import List
import requests
import json



def call_wepoints(messages: List[dict],
                 temperature: float = 0.0,
                 max_new_tokens: int = 2048,
                 repetition_penalty: float = 1.05,
                 top_p: float = 0.8,
                 top_k: int = 20,
                 do_sample: bool = True,
                 url: str = 'http://127.0.0.1:8081/v1/chat/completions') -> str:
    """Query WePOINTS model to generate a response.

    Args:
        messages (List[dict]): A list of messages to be sent to WePOINTS. The
            messages should be the standard OpenAI messages, like:
            [
                {
                    'role': 'user',
                    'content': [
                        {
                            'type': 'text',
                            'text': 'Please describe this image in short'
                        },
                        {
                            'type': 'image_url',
                            'image_url': {'url': /path/to/image.jpg}
                        }
                    ]
                }
            ]
        temperature (float, optional): The temperature of the model.
            Defaults to 0.0.
        max_new_tokens (int, optional): The maximum number of new tokens to generate.
            Defaults to 2048.
        repetition_penalty (float, optional): The penalty for repetition.
            Defaults to 1.05.
        top_p (float, optional): The top-p probability threshold.
            Defaults to 0.8.
        top_k (int, optional): The top-k sampling vocabulary size.
            Defaults to 20.
        do_sample (bool, optional): Whether to use sampling or greedy decoding.
            Defaults to True.
        url (str, optional): The URL of the WePOINTS model.
            Defaults to 'http://127.0.0.1:8081/v1/chat/completions'.

    Returns:
        str: The generated response from WePOINTS.
    """
    data = {
        'model': 'WePoints',
        'messages': messages,
        'max_new_tokens': max_new_tokens,
        'temperature': temperature,
        'repetition_penalty': repetition_penalty,
        'top_p': top_p,
        'top_k': top_k,
        'do_sample': do_sample,
    }
    response = requests.post(url,
                             json=data)
    response = json.loads(response.text)
    response = response['choices'][0]['message']['content']
    return response

system_prompt_point = (
    'You are a GUI agent. Based on the UI screenshot provided, please locate the exact position of the element that matches the instruction given by the user.\n\n'
    'Requirements for the output:\n'
    '- Return only the point (x, y) representing the center of the target element\n'
    '- Coordinates must be normalized to the range [0, 1]\n'
    '- Round each coordinate to three decimal places\n'
    '- Format the output as strictly (x, y) without any additional text\n'
)
system_prompt_bbox = (
    'You are a GUI agent. Based on the UI screenshot provided, please output the bounding box of the element that matches the instruction given by the user.\n\n'
    'Requirements for the output:\n'
    '- Return only the bounding box coordinates (x0, y0, x1, y1)\n'
    '- Coordinates must be normalized to the range [0, 1]\n'
    '- Round each coordinate to three decimal places\n'
    '- Format the output as strictly (x0, y0, x1, y1) without any additional text.\n'
)
system_prompt = system_prompt_point  # system_prompt_bbox
user_prompt = None  # replace with your instruction (e.g., 'close the window')

messages = [
            {
              'role': 'system',
              'content': [
                  {
                      'type': 'text',
                      'text': system_prompt
                  }
              ]
            },
            {
              'role': 'user',
              'content': [
                  {
                      'type': 'image_url',
                      'image_url': {'url': '/path/to/image.jpg'}
                  },
                  {
                      'type': 'text',
                      'text': user_prompt
                  }
              ]
            }
           ]
response = call_wepoints(messages)
print(response)
```

## Citation

If you use this model in your work, please cite the following paper:

```
@article{zhao2026pointsguigguigroundingjourney,
  title   = {POINTS-GUI-G: GUI-Grounding Journey},
  author  = {Zhao, Zhongyin and Liu, Yuan and Liu, Yikun and Wang, Haicheng and Tian, Le and Zhou, Xiao and You, Yangxiu and Yu, Zilin and Yu, Yang and Zhou, Jie},
  journal = {arXiv preprint arXiv:2602.06391},
  year    = {2026}
}

@inproceedings{liu2025points,
  title={POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion},
  author={Liu, Yuan and Zhao, Zhongyin and Tian, Le and Wang, Haicheng and Ye, Xubing and You, Yangxiu and Yu, Zilin and Wu, Chuhan and Xiao, Zhou and Yu, Yang and others},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  pages={1576--1601},
  year={2025}
}

@article{liu2024points1,
  title={POINTS1. 5: Building a Vision-Language Model towards Real World Applications},
  author={Liu, Yuan and Tian, Le and Zhou, Xiao and Gao, Xinyu and Yu, Kavio and Yu, Yang and Zhou, Jie},
  journal={arXiv preprint arXiv:2412.08443},
  year={2024}
}

@article{liu2024points,
  title={POINTS: Improving Your Vision-language Model with Affordable Strategies},
  author={Liu, Yuan and Zhao, Zhongyin and Zhuang, Ziyuan and Tian, Le and Zhou, Xiao and Zhou, Jie},
  journal={arXiv preprint arXiv:2409.04828},
  year={2024}
}

@article{liu2024rethinking,
  title={Rethinking Overlooked Aspects in Vision-Language Models},
  author={Liu, Yuan and Tian, Le and Zhou, Xiao and Zhou, Jie},
  journal={arXiv preprint arXiv:2405.11850},
  year={2024}
}
```