---
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-7B-Instruct
base_model_relation: finetune
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- hyperclick
- gui-grounding
- multimodal
- confidence-calibration
- reinforcement-learning
- arxiv:2510.27266
---
# HyperClick-7B
[Paper](https://arxiv.org/pdf/2510.27266) · [Code](https://github.com/xiaomi-research/hyperclick) · [3B model](https://huggingface.co/SeerRay-Lab/Qwen2.5-VL-3B-HyperClick) · [7B model](https://huggingface.co/SeerRay-Lab/Qwen2.5-VL-7B-HyperClick)
## Overview
HyperClick grounds natural-language instructions in GUI screenshots and predicts a click point together with an explicit confidence score. This repository contains the **7B checkpoint**, based on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), with full model weights in Safetensors format.
The training framework combines supervised fine-tuning with reinforcement fine-tuning. Its rewards check output format, grounding correctness, and confidence alignment using a truncated Gaussian spatial target and the Brier score. See the [training code](https://github.com/xiaomi-research/hyperclick/blob/main/src/open-r1-multimodal/src/open_r1/hyperclick.py) for details.
## Reported results
Grounding accuracy (%) from the [project evaluation table](https://github.com/xiaomi-research/hyperclick#evaluation). These are the project's reported results, not a new evaluation of the uploaded files.
| Model | ScreenSpot | ScreenSpot-v2 | ScreenSpot-Pro | MMBench-GUI | UI-I2E-Bench | CAGUI | UI-Vision |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| HyperClick-3B | 88.5 | 90.6 | 41.3 | 71.4 | 71.8 | 81.0 | 19.6 |
| HyperClick-7B | 91.5 | 93.7 | 48.2 | 79.6 | 76.5 | 82.9 | 25.7 |
## Quick start
The example below uses Transformers on a CUDA GPU. Install PyTorch for your CUDA environment, then install the inference dependencies:
```bash
pip install "transformers==4.49.0" "accelerate==1.10.0" "qwen-vl-utils==0.0.11" pillow
```
Replace `screenshot.png` and the instruction with your own input. The prompt follows the HyperClick training template.
```python
import torch
from PIL import Image
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
model_id = "SeerRay-Lab/Qwen2.5-VL-7B-HyperClick"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_id, torch_dtype=torch.bfloat16, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(
model_id, min_pixels=3136, max_pixels=4390400, use_fast=False
)
screenshot = Image.open("screenshot.png").convert("RGB")
instruction = "Click the search button"
messages = [dict(role="user", content=[
dict(type="image", image=screenshot, min_pixels=3136, max_pixels=4390400),
dict(type="text", text=(
f'Point to the element related to the instruction "{instruction}" '
'on the screenshot with your confidence.'
)),
])]
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
images, _ = process_vision_info(messages)
inputs = processor(text=[prompt], images=images, return_tensors="pt").to(model.device)
with torch.inference_mode():
generated = model.generate(**inputs, max_new_tokens=128, do_sample=False)
answer = processor.batch_decode(
generated[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0]
print(answer)
# Dimensions of the image coordinate space used by the model.
patch_size = processor.image_processor.patch_size
_, grid_h, grid_w = inputs.image_grid_thw[0].tolist()
input_width, input_height = grid_w * patch_size, grid_h * patch_size
print("Model image size:", input_width, input_height)
```
### Output format and coordinates
The expected output format is:
```text
[x,y]conf
```
`x` and `y` are pixel coordinates in the processed image; `conf` is a confidence estimate between 0 and 1. To map a predicted point back to the original screenshot, use:
```python
# x and y are parsed from the model response.
x_original = x * screenshot.width / input_width
y_original = y * screenshot.height / input_height
```
Image resizing affects the coordinate system. Validate the response format and point bounds before using a prediction. Confidence is a learned estimate and can be incorrect, especially for unfamiliar interfaces or ambiguous instructions.
## Training
The [GitHub repository](https://github.com/xiaomi-research/hyperclick) provides training setup, example annotation formats, and the reinforcement fine-tuning entry points:
```bash
bash src/open-r1-multimodal/run_hyperclick_7b.sh
```
## License
This repository retains its Apache-2.0 license designation. See the [base model license](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/LICENSE).
## Citation
The latest arXiv version is titled *Enhancing Trustworthy GUI Grounding via Self-Critiqued Reinforcement Learning*.
```bibtex
@misc{zhang2025hyperclick,
title={Enhancing Trustworthy GUI Grounding via Self-Critiqued Reinforcement Learning},
author={Shaojie Zhang and Pei Fu and Ruoceng Zhang and Jiahui Yang and Anan Du and Xiuwen Xi and Shaokang Wang and Ying Huang and Bin Qin and Zhenbo Luo and Jian Luan},
year={2025},
eprint={2510.27266},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.27266}
}
```