tencent / POINTS-GUI-G

News

• 🔜 Upcoming: The End-to-End GUI Agent Model is currently under active development and will be released in a subsequent update. Stay tuned!
• 🚀 2026.02.06: We are happy to present POINTS-GUI-G , our specialized GUI Grounding Model. To facilitate reproducible evaluation, we provide comprehensive scripts and guidelines in our GitHub Repository .

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.

Examples

Prediction on desktop screenshots

Prediction on mobile screenshots

Prediction on web screenshots

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 using the following command:

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

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 for SGLang. You can check out this branch and install SGLang in editable mode by following the official guide 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:

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:

@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}
}