Instructions to use drunksu/GUISwiper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drunksu/GUISwiper with Transformers:

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

pipe = pipeline("image-text-to-text", model="drunksu/GUISwiper")
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("drunksu/GUISwiper")
model = AutoModelForMultimodalLM.from_pretrained("drunksu/GUISwiper", 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 drunksu/GUISwiper with vLLM:

Install from pip and serve model

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

SGLang

How to use drunksu/GUISwiper 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 "drunksu/GUISwiper" \
    --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": "drunksu/GUISwiper",
		"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 "drunksu/GUISwiper" \
        --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": "drunksu/GUISwiper",
		"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 drunksu/GUISwiper with Docker Model Runner:
```
docker model run hf.co/drunksu/GUISwiper
```

GUISwiper 3B (RL)

GUISwiper is an RL-aligned GUI agent model for human-like swipe execution, introduced in the paper SwipeGen: Bridging the Execution Gap in GUI Agents via Human-like Swipe Synthesis (ACM MM 2026 Oral).

This repository hosts the final RL-aligned 3B checkpoint (bfloat16), fine-tuned from Qwen/Qwen2.5-VL-3B-Instruct and evaluated on SwipeBench.

Model Details

Property	Value
Base model	Qwen/Qwen2.5-VL-3B-Instruct
Parameters	3B (bfloat16, ~7 GB)
Training	RL alignment
Evaluation	SwipeBench
Input	GUI screenshots / screen videos + instruction
Output	Human-like swipe action (trajectory)
Hardware	NVIDIA GPUs (see paper for details)

Usage

import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor

repo_id = "drunksu/GUISwiper"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    repo_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(repo_id)

# image (GUI screenshot) + instruction -> swipe trajectory
# (follow the prompt format in the SwipeGen repo for the full inference pipeline)
image = load_your_gui_screenshot()  # PIL.Image
messages = [{"role": "user", "content": [
    {"type": "image", "image": image},
    {"type": "text", "text": "Describe the swipe to perform here."},
]}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256)
print(processor.batch_decode(output, skip_special_tokens=True)[0])

Requires transformers >= 4.49.0. See the SwipeGen GitHub repo for the complete inference and evaluation pipeline.

Download a Single File

from huggingface_hub import hf_hub_download

path = hf_hub_download("drunksu/GUISwiper", "model-00001-of-00002.safetensors")

Citation

@misc{swipegen2026,
  title  = {SwipeGen: Bridging the Execution Gap in GUI Agents via Human-like Swipe Synthesis},
  author = {SwipeGen Team},
  journal = {arXiv preprint arXiv:2601.18305},
  year   = {2026},
  note   = {Code and models: \url{https://github.com/TSKGHS17/SwipeGen}}
}

If you use GUISwiper, please also reference the official repository: https://github.com/TSKGHS17/SwipeGen.

License & Disclaimer

The model weights are released under Apache-2.0, consistent with the base model Qwen2.5-VL-3B-Instruct. Users should comply with the original license terms of Qwen2.5-VL and use the model responsibly; outputs are generated by AI and may contain errors.

Downloads last month: 16

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for drunksu/GUISwiper

Base model

Qwen/Qwen2.5-VL-3B-Instruct

Finetuned

(916)

this model

Paper for drunksu/GUISwiper

SwipeGen: Bridging the Execution Gap in GUI Agents via Human-like Swipe Synthesis

Paper • 2601.18305 • Published Jan 26

drunksu
/

GUISwiper