HelloKKMe
/

GTA1-72B

@@ -3,197 +3,126 @@ library_name: transformers
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 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              |        —          |
+| Grounding-R1 (Ours)              | 7B       | ✅              | 92.4 <sub>*(∆ +2.7)*</sub>             | 50.1<sub>*(∆ +8.1)*</sub>              | 67.7 <sub>*(∆ +3.5)*</sub>              |
+| Grounding-R1 (Ours)              | 32B      | ✅              | 93.2 <sub>*(∆ +1.3)*</sub>             | 53.6 <sub>*(∆ +5.6)*</sub>             |        61.9<sub>*(∆ +2.3)*</sub>          |
+| Grounding-R1 (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/grounding-r1-72B"
+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/Grounding-R1) for more details.