Instructions to use PrentisAI/GroundGUI-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PrentisAI/GroundGUI-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="PrentisAI/GroundGUI-8B")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("PrentisAI/GroundGUI-8B")
model = AutoModelForMultimodalLM.from_pretrained("PrentisAI/GroundGUI-8B", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PrentisAI/GroundGUI-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PrentisAI/GroundGUI-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrentisAI/GroundGUI-8B",
		"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/PrentisAI/GroundGUI-8B

SGLang

How to use PrentisAI/GroundGUI-8B 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 "PrentisAI/GroundGUI-8B" \
    --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": "PrentisAI/GroundGUI-8B",
		"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 "PrentisAI/GroundGUI-8B" \
        --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": "PrentisAI/GroundGUI-8B",
		"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 PrentisAI/GroundGUI-8B with Docker Model Runner:
```
docker model run hf.co/PrentisAI/GroundGUI-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GroundGUI-8B

A GUI grounding model built on Qwen3-VL-8B-Instruct. Given a screenshot and a natural-language instruction, it outputs a single click action as a <tool_call> with a coordinate on a 0–1000 grid.

Code: PrentisAI/GroundGUI
Data: PrentisAI/ScreenRef

Training

Fine-tuned from Qwen3-VL-8B-Instruct on ScreenRef. Details will be described in the paper.

Results

Accuracy (%) is the fraction of predicted click points that land inside the ground-truth box. Coordinates are mapped back to pixel space and checked against the box exactly, with no rounding tolerance.

ScreenSpot-v2	ScreenSpot-Pro	MMBench-GUI L2	UI-Vision	OSWorld-G
95.2 (1211/1272)	63.9 (1011/1581)	86.8 (3121/3594)	38.3 (2101/5479)	65.4 (369/564)

Inference used vLLM with greedy decoding (temperature 0) and max_tokens 1024.
OSWorld-G is the original 564-question split.

Usage

The model expects a specific system prompt, and the chat template does not add it for you. Use the evaluation code in GroundGUI (scripts/eval.sh) to reproduce the numbers above; it sets the system prompt (scalecua_toolcall) and the scoring for each benchmark.

The output looks like:

<tool_call>
{"name": "computer_use", "arguments": {"action": "left_click", "coordinate": [x, y]}}
</tool_call>

To get pixel coordinates from the output: x_px = x / 1000 * image_width and y_px = y / 1000 * image_height.

Notes

The tool name depends on the platform: computer_use for desktop, browser_use for web, mobile_use for mobile.
generation_config.json is inherited from the base model and enables sampling. Pass do_sample=False to reproduce the numbers above.
The weights were saved with transformers 5.12.

License

Apache License 2.0, the same as the base model. This model is a modified version of Qwen3-VL-8B-Instruct: Prentis AI fine-tuned the original weights.

The training data, ScreenRef, is distributed under its own terms; this license covers the model only and grants no rights in the dataset or in the screenshots it contains.

Citation

If you use this model, please cite the ScreenRef dataset: PrentisAI/ScreenRef. A BibTeX entry will be added when the paper is public.

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Safetensors

Model size

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Tensor type

BF16

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