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drunksu/GUISwiper
GUISwiper is a image-text-to-text model from drunksu. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
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).
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From the Hugging Face model README
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.
| 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) |
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.
from huggingface_hub import hf_hub_download
path = hf_hub_download("drunksu/GUISwiper", "model-00001-of-00002.safetensors")
@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.
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.