Instructions to use OS-Copilot/OS-Genesis-4B-AC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OS-Copilot/OS-Genesis-4B-AC with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="OS-Copilot/OS-Genesis-4B-AC", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OS-Copilot/OS-Genesis-4B-AC", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OS-Copilot/OS-Genesis-4B-AC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OS-Copilot/OS-Genesis-4B-AC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OS-Copilot/OS-Genesis-4B-AC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/OS-Copilot/OS-Genesis-4B-AC

SGLang

How to use OS-Copilot/OS-Genesis-4B-AC with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OS-Copilot/OS-Genesis-4B-AC" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OS-Copilot/OS-Genesis-4B-AC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OS-Copilot/OS-Genesis-4B-AC" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OS-Copilot/OS-Genesis-4B-AC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use OS-Copilot/OS-Genesis-4B-AC with Docker Model Runner:
```
docker model run hf.co/OS-Copilot/OS-Genesis-4B-AC
```

QiushiSun commited on Dec 31, 2024

Commit

5d7b271

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1 Parent(s): 4bde4fb

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@@ -5,40 +5,42 @@ base_model: OpenGVLab/InternVL2-4B
 pipeline_tag: image-text-to-text
 ---
-# OS-Atlas: A Foundation Action Model For Generalist GUI Agents
 <div align="center">
-[\[🏠Homepage\]](https://osatlas.github.io) [\[💻Code\]](https://github.com/OS-Copilot/OS-Atlas) [\[🚀Quick Start\]](#quick-start) [\[📝Paper\]](https://arxiv.org/abs/2410.23218) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-atlas-67246e44003a1dfcc5d0d045)[\[🤗Data\]](https://huggingface.co/datasets/OS-Copilot/OS-Atlas-data) [\[🤗ScreenSpot-v2\]](https://huggingface.co/datasets/OS-Copilot/ScreenSpot-v2)
 </div>
 ## Overview
-![os-atlas](https://github.com/user-attachments/assets/cf2ee020-5e15-4087-9a7e-75cc43662494)
-OS-Atlas provides a series of models specifically designed for GUI agents.
-For GUI grounding tasks, you can use:
-- [OS-Atlas-Base-7B](https://huggingface.co/OS-Copilot/OS-Atlas-Base-7B)
-- [OS-Atlas-Base-4B](https://huggingface.co/OS-Copilot/OS-Atlas-Base-4B)
-For generating single-step actions in GUI agent tasks, you can use:
-- [OS-Atlas-Pro-7B](https://huggingface.co/OS-Copilot/OS-Atlas-Pro-7B)
-- [OS-Atlas-Pro-4B](https://huggingface.co/OS-Copilot/OS-Atlas-Pro-4B)
-## Quick Start
-OS-Atlas-Base-4B is a GUI grounding model finetuned from [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B).
-**Notes:** Our models accept images of any size as input. The model outputs are normalized to relative coordinates within a 0-1000 range (either a center point or a bounding box defined by top-left and bottom-right coordinates). For visualization, please remember to convert these relative coordinates back to the original image dimensions.
 ### Inference Example
 First, install the `transformers` library:
 ```
 pip install transformers
 ```
-For additional dependencies, please refer to the [InternVL2 documentation](https://internvl.readthedocs.io/en/latest/get_started/installation.html)
-Then download the [example image](https://github.com/OS-Copilot/OS-Atlas/blob/main/examples/images/web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png) and save it to the current directory.
 Inference code example:
 ```python
@@ -135,21 +137,19 @@ tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast
 pixel_values = load_image('./web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png', max_num=6).to(torch.bfloat16).cuda()
 generation_config = dict(max_new_tokens=1024, do_sample=True)
-question = "In the screenshot of this web page, please give me the coordinates of the element I want to click on according to my instructions(with point).\n\"'Champions League' link\""
 response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
 print(f'User: {question}\nAssistant: {response}')
 ```
 ## Citation
 If you find this repository helpful, feel free to cite our paper:
 ```bibtex
-@article{wu2024atlas,
-        title={OS-ATLAS: A Foundation Action Model for Generalist GUI Agents},
-        author={Wu, Zhiyong and Wu, Zhenyu and Xu, Fangzhi and Wang, Yian and Sun, Qiushi and Jia, Chengyou and Cheng, Kanzhi and Ding, Zichen and Chen, Liheng and Liang, Paul Pu and others},
-        journal={arXiv preprint arXiv:2410.23218},
-        year={2024}
-      }
 ```

 pipeline_tag: image-text-to-text
 ---
+# OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
 <div align="center">
+[\[🏠Homepage\]](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)
 </div>
 ## Overview
+![os-genesis](https://cdn-uploads.huggingface.co/production/uploads/6064a0eeb1703ddba0d458b9/XvcAh92uvJQglmIu_L_nK.png)
+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.
+## Quick Start
+OS-Genesis-4B-AC is a mobile action model finetuned from [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B).
+###  OS-Genesis AC Family Models
+In the following table, we provide an overview of the OS-Genesis AC Family Models used for evaluating the AndroidControl Benchmark.
+|   Model Name    |                           Base Model                                            |                           Training Data                                            |                           HF Link                           |
+| :-------------: | :-------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------: |
+| OS-Genesis-4B-AC | [InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B)            | [OS-Genesis-mobile-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-4B-AC)  |
+| OS-Genesis-7B-AC | [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | [OS-Genesis-mobile-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-7B-AC)  |
+| OS-Genesis-8B-AC | [InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-8B)            | [OS-Genesis-mobile-data](https://huggingface.co/datasets/OS-Copilot/OS-Genesis-mobile-data) | [🤗 link](https://huggingface.co/OS-Copilot/OS-Genesis-8B-AC)  |
 ### Inference Example
 First, install the `transformers` library:
 ```
 pip install transformers
 ```
+For additional dependencies, please refer to the [InternVL2 documentation](https://internvl.readthedocs.io/en/latest/get_started/installation.html).
+For evaluating the AndroidControl Benchmark, please refer to the [**evaluation code**](https://github.com/OS-Copilot/OS-Genesis/tree/main/evaluation/android_control).
 Inference code example:
 ```python
 pixel_values = load_image('./web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png', max_num=6).to(torch.bfloat16).cuda()
 generation_config = dict(max_new_tokens=1024, do_sample=True)
+question = "<image> 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."
 response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
 print(f'User: {question}\nAssistant: {response}')
 ```
 ## Citation
 If you find this repository helpful, feel free to cite our paper:
 ```bibtex
+@article{sun2024osgenesis,
+  title={OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis},
+  author={Qiushi Sun and Kanzhi Cheng and Zichen Ding and Chuanyang Jin and Yian Wang and Fangzhi Xu and Zhenyu Wu and Chengyou Jia and Liheng Chen and Zhoumianze Liu and Ben Kao and Guohao Li and Junxian He and Yu Qiao and Zhiyong Wu},
+  journal={arXiv preprint arXiv:2412.19723},
+  year={2024}
+}
 ```