README.md

---
library_name: transformers
tags: []
---

# Introduction

Reinforcement learning (RL) (e.g., GRPO) helps with grounding because of its inherent objective alignment—rewarding successful clicks—rather than encouraging long textual Chain-of-Thought (CoT) reasoning. Unlike approaches that rely heavily on verbose CoT reasoning, GRPO directly incentivizes actionable and grounded responses. Based on findings from our [blog](https://huggingface.co/blog/HelloKKMe/grounding-r1), we share state-of-the-art GUI grounding models trained using GRPO.

# Performance

We follow the standard evaluation protocol and benchmark our model on three challenging datasets. Our method consistently achieves the best results among all open-source model families. Below are the comparative results:

| **Model**         | **Size** | **Open Source** | **ScreenSpot-V2** | **ScreenSpotPro** | **OSWORLD-G** |
|-------------------|:--------:|:---------------:|:-----------------:|:-----------------:|:-----------------:|
| OpenAI CUA        | —        | ❌              | 87.9              | 23.4              |        —          |
| Claude 3.7        | —        | ❌              | 87.6              | 27.7              |        —          | 
| JEDI-7B           | 7B       | ✅              | 91.7              | 39.5              | 54.1              |
| SE-GUI            | 7B       | ✅              | 90.3              | 47.0              |        —          |
| UI-TARS           | 7B       | ✅              | 91.6              | 35.7              | 47.5              |
| UI-TARS-1.5*       | 7B       | ✅              | 89.7*                 | 42.0*              |  64.2* | 
| UGround-v1-7B     | 7B       | ✅              |  —                | 31.1              |   36.4        |
| Qwen2.5-VL-32B-Instruct | 32B | ✅              |  91.9*                | 48.0              |        59.6*      |    |
| UGround-v1-72B    | 72B      | ✅              |  —                | 34.5              |        —          |
| Qwen2.5-VL-72B-Instruct | 72B | ✅              |  94.00*                | 53.3              |        62.2*          |
| UI-TARS           | 72B      | ✅              | 90.3              | 38.1              |        —          |
| GTA1 (Ours)              | 7B       | ✅              | 92.4 <sub>*(∆ +2.7)*</sub>             | 50.1<sub>*(∆ +8.1)*</sub>              | 67.7 <sub>*(∆ +3.5)*</sub>              |
| GTA1 (Ours)              | 32B      | ✅              | 93.2 <sub>*(∆ +1.3)*</sub>             | 53.6 <sub>*(∆ +5.6)*</sub>             |        61.9<sub>*(∆ +2.3)*</sub>          |
| GTA1 (Ours)              | 72B      | ✅              | 94.8<sub>*(∆ +0.8)*</sub>              | 58.4 <sub>*(∆ +5.1)*</sub>             |        66.7<sub>*(∆ +4.5)*</sub>          |


> **Note:**  
> - Model size is indicated in billions (B) of parameters.  
> - A dash (—) denotes results that are currently unavailable.  
> - A superscript asterisk (﹡) denotes our evaluated result.
> - UI-TARS-1.5 7B, Qwen2.5-VL-32B-Instruct, and Qwen2.5-VL-72B-Instruct are applied as our baseline models.
> - ∆ indicates the performance improvement (∆) of our model compared to its baseline.

# Inference
Below is a code snippet demonstrating how to run inference using a trained model.

```python
from PIL import Image
from qwen_vl_utils import process_vision_info, smart_resize
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
import torch
import re

SYSTEM_PROMPT = '''
You are an expert UI element locator. Given a GUI image and a user's element description, provide the coordinates of the specified element as a single (x,y) point. The image resolution is height {height} and width {width}. For elements with area, return the center point.

Output the coordinate pair exactly:
(x,y)
'''
SYSTEM_PROMPT=SYSTEM_PROMPT.strip()

# Function to extract coordinates from model output
def extract_coordinates(raw_string):
    try:
        matches = re.findall(r"\((-?\d*\.?\d+),\s*(-?\d*\.?\d+)\)", raw_string)
        return [tuple(map(int, match)) for match in matches][0]
    except:
        return 0,0

# Load model and processor
model_path = "HelloKKMe/GTA1-72B"
max_new_tokens = 32

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(
    model_path,
    min_pixels=3136,
    max_pixels= 4096 * 2160
)

# Load and resize image
image = Image.open("file path")
instruction = "description"  # Instruction for grounding
width, height = image.width, image.height

resized_height, resized_width = smart_resize(
    image.height,
    image.width,
    factor=processor.image_processor.patch_size * processor.image_processor.merge_size,
    min_pixels=processor.image_processor.min_pixels,
    max_pixels=processor.image_processor.max_pixels,
)
resized_image = image.resize((resized_width, resized_height))
scale_x, scale_y = width / resized_width, height / resized_height

# Prepare system and user messages
system_message = {
   "role": "system",
   "content": SYSTEM_PROMPT.format(height=resized_height,width=resized_width)
}

user_message = {
    "role": "user",
    "content": [
        {"type": "image", "image": resized_image},
        {"type": "text", "text": instruction}
    ]
}

# Tokenize and prepare inputs
image_inputs, video_inputs = process_vision_info([system_message, user_message])
text = processor.apply_chat_template([system_message, user_message], tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
inputs = inputs.to(model.device)

# Generate prediction
output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=1.0, use_cache=True)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]

# Extract and rescale coordinates
pred_x, pred_y  = extract_coordinates(output_text) 
pred_x*=scale_x
pred_y*=scale_y 
print(pred_x,pred_y)
```

Refer to our [code](https://github.com/Yan98/GTA1) for more details.