初始化项目，由ModelHub XC社区提供模型
Model: HelloKKMe/GTA1-7B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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library_name: transformers
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tags: []
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---
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# Introduction
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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.
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# Performance
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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:
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| **Model** | **Size** | **Open Source** | **ScreenSpot-V2** | **ScreenSpotPro** | **OSWORLD-G** |
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|-------------------|:--------:|:---------------:|:-----------------:|:-----------------:|:-----------------:|
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| OpenAI CUA | — | ❌ | 87.9 | 23.4 | — |
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| Claude 3.7 | — | ❌ | 87.6 | 27.7 | — |
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| JEDI-7B | 7B | ✅ | 91.7 | 39.5 | 54.1 |
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| SE-GUI | 7B | ✅ | 90.3 | 47.0 | — |
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| UI-TARS | 7B | ✅ | 91.6 | 35.7 | 47.5 |
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| UI-TARS-1.5* | 7B | ✅ | 89.7* | 42.0* | 64.2* |
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| UGround-v1-7B | 7B | ✅ | — | 31.1 | 36.4 |
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| Qwen2.5-VL-32B-Instruct | 32B | ✅ | 91.9* | 48.0 | 59.6* | |
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| UGround-v1-72B | 72B | ✅ | — | 34.5 | — |
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| Qwen2.5-VL-72B-Instruct | 72B | ✅ | 94.00* | 53.3 | 62.2* |
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| UI-TARS | 72B | ✅ | 90.3 | 38.1 | — |
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| GTA1 (Ours) | 7B | ✅ | 92.4 <sub>*(∆ +2.7)*</sub> | 50.1<sub>*(∆ +8.1)*</sub> | 67.7 <sub>*(∆ +3.5)*</sub> |
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| GTA1 (Ours) | 32B | ✅ | 93.2 <sub>*(∆ +1.3)*</sub> | 53.6 <sub>*(∆ +5.6)*</sub> | 61.9<sub>*(∆ +2.3)*</sub> |
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| GTA1 (Ours) | 72B | ✅ | 94.8<sub>*(∆ +0.8)*</sub> | 58.4 <sub>*(∆ +5.1)*</sub> | 66.7<sub>*(∆ +4.5)*</sub> |
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> **Note:**
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> - Model size is indicated in billions (B) of parameters.
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> - A dash (—) denotes results that are currently unavailable.
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> - A superscript asterisk (﹡) denotes our evaluated result.
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> - UI-TARS-1.5 7B, Qwen2.5-VL-32B-Instruct, and Qwen2.5-VL-72B-Instruct are applied as our baseline models.
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> - ∆ indicates the performance improvement (∆) of our model compared to its baseline.
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# Inference
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Below is a code snippet demonstrating how to run inference using a trained model.
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```python
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from PIL import Image
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from qwen_vl_utils import process_vision_info, smart_resize
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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import torch
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import re
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SYSTEM_PROMPT = '''
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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.
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Output the coordinate pair exactly:
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(x,y)
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'''
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SYSTEM_PROMPT=SYSTEM_PROMPT.strip()
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# Function to extract coordinates from model output
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def extract_coordinates(raw_string):
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try:
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matches = re.findall(r"\((-?\d*\.?\d+),\s*(-?\d*\.?\d+)\)", raw_string)
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return [tuple(map(int, match)) for match in matches][0]
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except:
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return 0,0
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# Load model and processor
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model_path = "HelloKKMe/GTA1-7B"
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max_new_tokens = 32
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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device_map="auto"
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)
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processor = AutoProcessor.from_pretrained(
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model_path,
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min_pixels=3136,
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max_pixels= 4096 * 2160
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)
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# Load and resize image
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image = Image.open("file path")
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instruction = "description" # Instruction for grounding
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width, height = image.width, image.height
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resized_height, resized_width = smart_resize(
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image.height,
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image.width,
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factor=processor.image_processor.patch_size * processor.image_processor.merge_size,
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min_pixels=processor.image_processor.min_pixels,
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max_pixels=processor.image_processor.max_pixels,
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)
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resized_image = image.resize((resized_width, resized_height))
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scale_x, scale_y = width / resized_width, height / resized_height
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# Prepare system and user messages
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system_message = {
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"role": "system",
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"content": SYSTEM_PROMPT.format(height=resized_height,width=resized_width)
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}
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user_message = {
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"role": "user",
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"content": [
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{"type": "image", "image": resized_image},
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{"type": "text", "text": instruction}
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]
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}
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# Tokenize and prepare inputs
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image_inputs, video_inputs = process_vision_info([system_message, user_message])
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text = processor.apply_chat_template([system_message, user_message], tokenize=False, add_generation_prompt=True)
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inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
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inputs = inputs.to(model.device)
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# Generate prediction
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output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=1.0, use_cache=True)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
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# Extract and rescale coordinates
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pred_x, pred_y = extract_coordinates(output_text)
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pred_x*=scale_x
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pred_y*=scale_y
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print(pred_x,pred_y)
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```
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Refer to our [code](https://github.com/Yan98/GTA1) for more details.
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added_tokens.json
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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config.json
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Normal file
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31
special_tokens_map.json
Normal file
31
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Normal file
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215
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215
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"max_length": null,
|
||||
"max_pixels": 8847360,
|
||||
"min_pixels": 3136,
|
||||
"model_max_length": 131072,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "right",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user