Instructions to use macpaw-research/GUIrilla-See-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macpaw-research/GUIrilla-See-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="macpaw-research/GUIrilla-See-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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("macpaw-research/GUIrilla-See-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use macpaw-research/GUIrilla-See-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macpaw-research/GUIrilla-See-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": "macpaw-research/GUIrilla-See-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/macpaw-research/GUIrilla-See-7B

SGLang

How to use macpaw-research/GUIrilla-See-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 "macpaw-research/GUIrilla-See-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": "macpaw-research/GUIrilla-See-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 "macpaw-research/GUIrilla-See-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": "macpaw-research/GUIrilla-See-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 macpaw-research/GUIrilla-See-7B with Docker Model Runner:
```
docker model run hf.co/macpaw-research/GUIrilla-See-7B
```

GUIrilla-See-7B / README.md

Krivoblotsky

meta: add model-index with GUIrilla-Task test success rate

b08a0c1 verified 3 months ago

preview code

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metadata

base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
pipeline_tag: image-text-to-text
model_name: GUIrilla-See-7B
tags:
  - generated_from_trainer
  - trl
  - sft
license: mit
datasets:
  - macpaw-research/GUIrilla-Task
model-index:
  - name: GUIrilla-See-7B
    results:
      - task:
          type: image-text-to-text
          name: GUI Grounding
        dataset:
          type: macpaw-research/GUIrilla-Task
          name: GUIrilla-Task
          split: test
        metrics:
          - type: accuracy
            value: 75.59
            name: Success Rate (%)

GUIrilla-See-7B

Vision–language grounding for graphical user interfaces

Summary

GUIrilla-See-7B is a 7 billion-parameter Qwen 2.5-VL model fine-tuned to locate on-screen elements of macOS GUI. Given a screenshot and a natural-language task, the model returns a single point (x, y) that lies at (or very near) the centre of the referenced region.

Quick-start

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
import torch, PIL.Image as Image

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "macpaw-research/GUIrilla-See-7B",
    torch_dtype="auto",
    device_map="auto",
    attn_implementation="flash_attention_2",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "macpaw-research/GUIrilla-See-7B",
    trust_remote_code=True,
    use_fast=True,
)

image = Image.open("screenshot.png")
task  = "the search field in the top-right corner"

conversation = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text",
         "text": (
             "Your task is to help the user identify the precise coordinates "
             "(x, y) of a specific area/element/object on the screen based on "
             "a description.\n"
             "- Your response should aim to point to the centre or a representative "
             "point within the described area/element/object as accurately as possible.\n"
             "- If the description is unclear or ambiguous, infer the most relevant area "
             "or element based on its likely context or purpose.\n"
             "- Your answer should be a single string (x, y) corresponding to the point "
             "of interest.\n"
             f"\nDescription: {task}"
             "\nAnswer:"
         )},
    ],
}]

texts        = processor.apply_chat_template(conversation, tokenize=False,
                                             add_generation_prompt=True)
image_inputs = [image]
inputs       = processor(text=texts, images=image_inputs,
                         return_tensors="pt", padding=True).to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=16, num_beams=3)

generated_ids = output_ids[:, inputs.input_ids.shape[1]:]
answer = processor.batch_decode(generated_ids,
                                skip_special_tokens=True)[0]
print("Predicted click:", answer)      # → "(812, 115)"

Training Data

Trained on GUIrilla-Task.

Train data: 25,606 tasks across 881 macOS applications (5% of apps from it for validation)
Test data: 1,565 tasks across 227 macOS applications

Training Procedure

2 epochs LoRA fine-tuning on 2 × H100 80 GB.
Optimiser – AdamW (β₁ = 0.9, β₂ = 0.95), LR = 2 e-5 with cosine decay and 0.05 warm up ratio.

Evaluation

Split	Success Rate %
Test	75.59

Ethical & Safety Notes

Always sandbox or use confirmation steps when connecting the model to real GUIs.
Screenshots may reveal sensitive data – ensure compliance with privacy regulations.

License

MIT (see LICENSE).

Citation

@article{garkot2025guirilla,
  title={GUIrilla: A Scalable Framework for Automated Desktop UI Exploration},
  author={Garkot, Sofiya and Shamrai, Maksym and Synytsia, Ivan and Hirna, Mariya},
  journal={arXiv preprint arXiv:2510.16051},
  year={2025},
  url={https://arxiv.org/abs/2510.16051}
}