Instructions to use microsoft/GUI-Actor-7B-Qwen2-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/GUI-Actor-7B-Qwen2-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/GUI-Actor-7B-Qwen2-VL")
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, Qwen2VLForConditionalGenerationWithPointer

processor = AutoProcessor.from_pretrained("microsoft/GUI-Actor-7B-Qwen2-VL")
model = Qwen2VLForConditionalGenerationWithPointer.from_pretrained("microsoft/GUI-Actor-7B-Qwen2-VL")
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 microsoft/GUI-Actor-7B-Qwen2-VL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/GUI-Actor-7B-Qwen2-VL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/GUI-Actor-7B-Qwen2-VL",
		"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/microsoft/GUI-Actor-7B-Qwen2-VL

SGLang

How to use microsoft/GUI-Actor-7B-Qwen2-VL 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 "microsoft/GUI-Actor-7B-Qwen2-VL" \
    --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": "microsoft/GUI-Actor-7B-Qwen2-VL",
		"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 "microsoft/GUI-Actor-7B-Qwen2-VL" \
        --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": "microsoft/GUI-Actor-7B-Qwen2-VL",
		"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 microsoft/GUI-Actor-7B-Qwen2-VL with Docker Model Runner:
```
docker model run hf.co/microsoft/GUI-Actor-7B-Qwen2-VL
```

GUI-Actor-7B-Qwen2-VL / README.md

qianhuiwu

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	---
	base_model:
	- Qwen/Qwen2-VL-7B-Instruct
	license: mit
	library_name: transformers
	pipeline_tag: image-text-to-text
	---

	# GUI-Actor-7B with Qwen2-VL-7B as backbone VLM

	This model was introduced in the paper [GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents](https://huggingface.co/papers/2506.03143).
	It is developed based on [Qwen2-VL-7B-Instruct ](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), augmented by an attention-based action head and finetuned to perform GUI grounding using the dataset [here](https://huggingface.co/datasets/cckevinn/GUI-Actor-Data).

	For more details on model design and evaluation, please check: [🏠 Project Page](https://microsoft.github.io/GUI-Actor/) \| [💻 Github Repo](https://github.com/microsoft/GUI-Actor) \| [📑 Paper](https://www.arxiv.org/pdf/2506.03143).

	\| Model Name \| Hugging Face Link \|
	\|--------------------------------------------\|--------------------------------------------\|
	\| GUI-Actor-7B-Qwen2-VL \| [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-7B-Qwen2-VL) \|
	\| GUI-Actor-2B-Qwen2-VL \| [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-2B-Qwen2-VL) \|
	\| GUI-Actor-7B-Qwen2.5-VL \| [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-7B-Qwen2.5-VL) \|
	\| GUI-Actor-3B-Qwen2.5-VL \| [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-3B-Qwen2.5-VL) \|
	\| GUI-Actor-7B-Qwen2.5-VL-LiteTrain \| [🤗 Hugging Face](https://huggingface.co/qianhuiwu/GUI-Actor-7B-Qwen2.5-VL-LiteTrain) \|
	\| GUI-Actor-3B-Qwen2.5-VL-LiteTrain \| [🤗 Hugging Face](https://huggingface.co/qianhuiwu/GUI-Actor-3B-Qwen2.5-VL-LiteTrain) \|
	\| GUI-Actor-Verifier-2B \| [🤗 Hugging Face](https://huggingface.co/microsoft/GUI-Actor-Verifier-2B) \|

	## 📊 Performance Comparison on GUI Grounding Benchmarks
	Table 1. Main results on ScreenSpot-Pro, ScreenSpot, and ScreenSpot-v2 with Qwen2-VL as the backbone. † indicates scores obtained from our own evaluation of the official models on Huggingface.
	\| Method \| Backbone VLM \| ScreenSpot-Pro \| ScreenSpot \| ScreenSpot-v2 \|
	\|------------------\|--------------\|----------------\|------------\|----------------\|
	\| _72B models:_
	\| AGUVIS-72B \| Qwen2-VL \| - \| 89.2 \| - \|
	\| UGround-V1-72B \| Qwen2-VL \| 34.5 \| 89.4 \| - \|
	\| UI-TARS-72B \| Qwen2-VL \| 38.1 \| 88.4 \| 90.3 \|
	\| _7B models:_
	\| OS-Atlas-7B \| Qwen2-VL \| 18.9 \| 82.5 \| 84.1 \|
	\| AGUVIS-7B \| Qwen2-VL \| 22.9 \| 84.4 \| 86.0† \|
	\| UGround-V1-7B \| Qwen2-VL \| 31.1 \| 86.3 \| 87.6† \|
	\| UI-TARS-7B \| Qwen2-VL \| 35.7 \| 89.5 \| 91.6 \|
	\| GUI-Actor-7B \| Qwen2-VL \| 40.7 \| 88.3 \| 89.5 \|
	\| GUI-Actor-7B + Verifier \| Qwen2-VL \| 44.2 \| 89.7 \| 90.9 \|
	\| _2B models:_
	\| UGround-V1-2B \| Qwen2-VL \| 26.6 \| 77.1 \| - \|
	\| UI-TARS-2B \| Qwen2-VL \| 27.7 \| 82.3 \| 84.7 \|
	\| GUI-Actor-2B \| Qwen2-VL \| 36.7 \| 86.5 \| 88.6 \|
	\| GUI-Actor-2B + Verifier \| Qwen2-VL \| 41.8 \| 86.9 \| 89.3 \|

	Table 2. Main results on the ScreenSpot-Pro and ScreenSpot-v2 with Qwen2.5-VL as the backbone.
	\| Method \| Backbone VLM \| ScreenSpot-Pro \| ScreenSpot-v2 \|
	\|----------------\|---------------\|----------------\|----------------\|
	\| _7B models:_
	\| Qwen2.5-VL-7B \| Qwen2.5-VL \| 27.6 \| 88.8 \|
	\| Jedi-7B \| Qwen2.5-VL \| 39.5 \| 91.7 \|
	\| GUI-Actor-7B \| Qwen2.5-VL \| 44.6 \| 92.1 \|
	\| GUI-Actor-7B + Verifier \| Qwen2.5-VL \| 47.7 \| 92.5 \|
	\| _3B models:_
	\| Qwen2.5-VL-3B \| Qwen2.5-VL \| 25.9 \| 80.9 \|
	\| Jedi-3B \| Qwen2.5-VL \| 36.1 \| 88.6 \|
	\| GUI-Actor-3B \| Qwen2.5-VL \| 42.2 \| 91.0 \|
	\| GUI-Actor-3B + Verifier \| Qwen2.5-VL \| 45.9 \| 92.4 \|

	## 🚀 Usage
	```python
	import torch

	from qwen_vl_utils import process_vision_info
	from datasets import load_dataset
	from transformers import Qwen2VLProcessor
	from gui_actor.constants import chat_template
	from gui_actor.modeling import Qwen2VLForConditionalGenerationWithPointer
	from gui_actor.inference import inference


	# load model
	model_name_or_path = "microsoft/GUI-Actor-7B-Qwen2-VL"
	data_processor = Qwen2VLProcessor.from_pretrained(model_name_or_path)
	tokenizer = data_processor.tokenizer
	model = Qwen2VLForConditionalGenerationWithPointer.from_pretrained(
	model_name_or_path,
	torch_dtype=torch.bfloat16,
	device_map="cuda:0",
	attn_implementation="flash_attention_2"
	).eval()

	# prepare example
	dataset = load_dataset("rootsautomation/ScreenSpot")["test"]
	example = dataset[0]
	print(f"Intruction: {example['instruction']}")
	print(f"ground-truth action region (x1, y1, x2, y2): {[round(i, 2) for i in example['bbox']]}")

	conversation = [
	{
	"role": "system",
	"content": [
	{
	"type": "text",
	"text": "You are a GUI agent. You are given a task and a screenshot of the screen. You need to perform a series of pyautogui actions to complete the task.",
	}
	]
	},
	{
	"role": "user",
	"content": [
	{
	"type": "image",
	"image": example["image"], # PIL.Image.Image or str to path
	# "image_url": "https://xxxxx.png" or "https://xxxxx.jpg" or "file://xxxxx.png" or "data:image/png;base64,xxxxxxxx", will be split by "base64,"
	},
	{
	"type": "text",
	"text": example["instruction"]
	},
	],
	},
	]

	# inference
	pred = inference(conversation, model, tokenizer, data_processor, use_placeholder=True, topk=3)
	px, py = pred["topk_points"][0]
	print(f"Predicted click point: [{round(px, 4)}, {round(py, 4)}]")

	# >> Model Response
	# Intruction: close this window
	# ground-truth action region (x1, y1, x2, y2): [0.9479, 0.1444, 0.9938, 0.2074]
	# Predicted click point: [0.9709, 0.1548]
	```

	## 📝 Citation
	```
	@article{wu2025gui,
	title={GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents},
	author={Wu, Qianhui and Cheng, Kanzhi and Yang, Rui and Zhang, Chaoyun and Yang, Jianwei and Jiang, Huiqiang and Mu, Jian and Peng, Baolin and Qiao, Bo and Tan, Reuben and others},
	journal={arXiv preprint arXiv:2506.03143},
	year={2025}
	}
	```