Instructions to use microsoft/GUI-Actor-Verifier-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/GUI-Actor-Verifier-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/GUI-Actor-Verifier-2B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("microsoft/GUI-Actor-Verifier-2B")
model = AutoModelForMultimodalLM.from_pretrained("microsoft/GUI-Actor-Verifier-2B")
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 Settings

vLLM

How to use microsoft/GUI-Actor-Verifier-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/GUI-Actor-Verifier-2B"
# 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-Verifier-2B",
		"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-Verifier-2B

SGLang

How to use microsoft/GUI-Actor-Verifier-2B 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-Verifier-2B" \
    --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-Verifier-2B",
		"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-Verifier-2B" \
        --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-Verifier-2B",
		"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-Verifier-2B with Docker Model Runner:
```
docker model run hf.co/microsoft/GUI-Actor-Verifier-2B
```

qianhuiwu commited on Jun 8, 2025

Commit

4c2ce70

verified ·

1 Parent(s): 3960674

highlight verifier numbers.

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README.md +2 -2

README.md CHANGED Viewed

@@ -60,8 +60,8 @@ Table 2. Main results on the ScreenSpot-Pro and ScreenSpot-v2 with **Qwen2.5-VL*
 | **_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
 The verifier takes a language instruction and an image with a red circle marking the target position as input. One example is shown below. It outputs either ‘True’ or ‘False’, and you can also use the probability of each label to score the sample.

 | **_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
 The verifier takes a language instruction and an image with a red circle marking the target position as input. One example is shown below. It outputs either ‘True’ or ‘False’, and you can also use the probability of each label to score the sample.