Instructions to use OpenGVLab/ZeroGUI-OSWorld-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/ZeroGUI-OSWorld-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/ZeroGUI-OSWorld-7B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("OpenGVLab/ZeroGUI-OSWorld-7B")
model = AutoModelForImageTextToText.from_pretrained("OpenGVLab/ZeroGUI-OSWorld-7B")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/ZeroGUI-OSWorld-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/ZeroGUI-OSWorld-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/ZeroGUI-OSWorld-7B",
		"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/OpenGVLab/ZeroGUI-OSWorld-7B

SGLang

How to use OpenGVLab/ZeroGUI-OSWorld-7B 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 "OpenGVLab/ZeroGUI-OSWorld-7B" \
    --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": "OpenGVLab/ZeroGUI-OSWorld-7B",
		"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 "OpenGVLab/ZeroGUI-OSWorld-7B" \
        --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": "OpenGVLab/ZeroGUI-OSWorld-7B",
		"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 OpenGVLab/ZeroGUI-OSWorld-7B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/ZeroGUI-OSWorld-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ZeroGUI-OSWorld-7B

[📜 Paper] [📂 GitHub]

Introduction

We propose ZeroGUI, a fully automated online reinforcement learning framework that enables GUI agents to train and adapt in interactive environments at zero human cost.

Automatic Task Generation: Automatically proposes diverse, executable GUI tasks.
Automatic Reward Estimation: Assigns binary task rewards based on trajectory screenshots and employs a voting mechanism to avoid hallucinated success.
Two-Stage Online RL: Combines training on generated tasks and test-time adaptation to continually improve agent's performance.

Results

Usage

Please refer to our Github Repo.

Citation

If you find this work helpful in your research, please consider citing:

@article{yang2025zerogui,
  title={ZeroGUI: Automating Online GUI Learning at Zero Human Cost},
  author={Yang, Chenyu and Shiqian, Su and Liu, Shi and Dong, Xuan and Yu, Yue and Su, Weijie and Wang, Xuehui and Liu, Zhaoyang and Zhu, Jinguo and Li, Hao and Wang, Wenhai and Qiao, Yu and Zhu, Xizhou and Dai, Jifeng},
  journal={arXiv preprint arXiv:2505.23762},
  year={2025}
}