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--- |
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license: apache-2.0 |
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library_name: transformers |
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base_model: Qwen/Qwen2-VL-7B-Instruct |
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pipeline_tag: image-text-to-text |
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--- |
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# OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis |
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<div align="center"> |
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[\[🏠Homepage\]](https://qiushisun.github.io/OS-Genesis-Home/) [\[💻Code\]](https://github.com/OS-Copilot/OS-Genesis) [\[📝Paper\]](https://arxiv.org/abs/2412.19723) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-genesis-6768d4b6fffc431dbf624c2d)[\[🤗Data\]](https://huggingface.co/collections/OS-Copilot/os-genesis-6768d4b6fffc431dbf624c2d) |
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</div> |
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## Overview |
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We introduce OS-Genesis, an interaction-driven pipeline that synthesizes high-quality and diverse GUI agent trajectory data without human supervision. By leveraging reverse task synthesis, OS-Genesis enables effective training of GUI agents to achieve superior performance on dynamic benchmarks such as AndroidWorld and WebArena. |
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## Quick Start |
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OS-Genesis-7B-AC is a mobile action model finetuned from [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct). |
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### OS-Genesis AC Family Models |
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In the following table, we provide an overview of the OS-Genesis AC Family Models used for evaluating the AndroidControl Benchmark. |
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| Model Name | Base Model | Training Data | HF Link | |
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| :-------------: | :-------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------: | |
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| OS-Genesis-4B-AC | [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-4B-AC) | |
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| OS-Genesis-7B-AC | [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-7B-AC) | |
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| OS-Genesis-8B-AC | [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B) | [OS-Genesis-ac-training-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data/blob/main/os_genesis_ac_training_data.jsonl) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-8B-AC) | |
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### Inference Example |
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First, ensure that the necessary dependencies are installed: |
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``` |
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pip install transformers |
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pip install qwen-vl-utils |
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``` |
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For evaluating the AndroidControl Benchmark, please refer to the [**evaluation code**](https://github.com/OS-Copilot/OS-Genesis/tree/main/evaluation/android_control). |
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Inference code example: |
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```python |
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from transformers import Qwen2VLForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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# Default: Load the model on the available device(s) |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"OS-Copilot/OS-Genesis-7B-AC", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("OS-Copilot/OS-Atlas-Base-7B") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "./web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", |
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}, |
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{"type": "text", "text": "You are a GUI task expert, I will provide you with a high-level instruction, an action history, a screenshot with its corresponding accessibility tree.\n High-level instruction: {high_level_instruction}\n Action history: {action_history}\n Accessibility tree: {a11y_tree}\n Please generate the low-level thought and action for the next step."}, |
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], |
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} |
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] |
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# Preparation for inference |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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# Inference: Generation of the output |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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# <|object_ref_start|>language switch<|object_ref_end|><|box_start|>(576,12),(592,42)<|box_end|><|im_end|> |
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``` |
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## Citation |
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If you find this repository helpful, feel free to cite our paper: |
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```bibtex |
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@article{sun2024genesis, |
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title={OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis}, |
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author={Sun, Qiushi and Cheng, Kanzhi and Ding, Zichen and Jin, Chuanyang and Wang, Yian and Xu, Fangzhi and Wu, Zhenyu and Jia, Chengyou and Chen, Liheng and Liu, Zhoumianze and others}, |
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journal={arXiv preprint arXiv:2412.19723}, |
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year={2024} |
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} |
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``` |
