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
```

GroundGUI-8B / README.md

tjcaspar

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3-VL-8B-Instruct
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- gui-agent
	- gui-grounding
	- qwen3-vl
	datasets:
	- PrentisAI/ScreenRef
	---

	# GroundGUI-8B

	A GUI grounding model built on [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/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.

	- Paper: [arXiv:2610.19100](https://arxiv.org/abs/2610.19100)
	- Code: [PrentisAI/GroundGUI](https://github.com/PrentisAI/GroundGUI)
	- Data: [PrentisAI/ScreenRef](https://huggingface.co/datasets/PrentisAI/ScreenRef)

	## Training

	Fine-tuned from Qwen3-VL-8B-Instruct on [ScreenRef](https://huggingface.co/datasets/PrentisAI/ScreenRef).
	Details are described in the [paper](https://arxiv.org/abs/2610.19100).

	## 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](https://github.com/PrentisAI/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](LICENSE), the same as the base model. This model is a modified version of
	[Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct): Prentis AI fine-tuned the
	original weights.

	The training data, [ScreenRef](https://huggingface.co/datasets/PrentisAI/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

	GroundGUI-8B is introduced in [GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning](https://arxiv.org/abs/2610.19100). If you use this model, please cite:

	```bibtex
	@misc{xie2026groundguiguigroundinghighquality,
	title={GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning},
	author={Qingsong Xie and Jiahao Tian and Jiacheng Tang and Zhifeng Li and Madalina Ciobanu and Navan Preet Singh and Qingqing Mao and Ritankar Das},
	year={2026},
	eprint={2610.19100},
	archivePrefix={arXiv},
	primaryClass={cs.AI},
	url={https://arxiv.org/abs/2610.19100},
	}
	```