Instructions to use LEONW24/BEPA-7B-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LEONW24/BEPA-7B-S2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="LEONW24/BEPA-7B-S2")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("LEONW24/BEPA-7B-S2")
model = AutoModelForMultimodalLM.from_pretrained("LEONW24/BEPA-7B-S2", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LEONW24/BEPA-7B-S2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LEONW24/BEPA-7B-S2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LEONW24/BEPA-7B-S2",
		"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/LEONW24/BEPA-7B-S2

SGLang

How to use LEONW24/BEPA-7B-S2 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 "LEONW24/BEPA-7B-S2" \
    --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": "LEONW24/BEPA-7B-S2",
		"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 "LEONW24/BEPA-7B-S2" \
        --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": "LEONW24/BEPA-7B-S2",
		"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 LEONW24/BEPA-7B-S2 with Docker Model Runner:
```
docker model run hf.co/LEONW24/BEPA-7B-S2
```

BEPA-7B-S2

File size: 3,732 Bytes

---
license: mit
library_name: transformers
pipeline_tag: image-text-to-text
base_model: bytedance-research/UI-TARS-1.5-7B
tags:
- gui-agent
- computer-use
- vision-language
- reinforcement-learning
- osworld
datasets:
- osworld
language:
- en
- zh
---

<h1 style="display: flex; align-items: center; justify-content: center; gap: 10px;">
  <img src="https://leon-gittech.github.io/Verl_GUI/icon.png" alt="BEPA" style="height: 0.9em; width: auto;">
  <span>BEPA-7B-S2</span>
</h1>

<p align="center">
  <b>From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation</b>
</p>

<p align="center">
  <a href="https://leon-gittech.github.io/Verl_GUI/">🌐 Project Page</a> |
  <a href="https://arxiv.org/abs/2601.05787">📑 arXiv Paper</a> |
  <a href="https://github.com/LEON-gittech/Verl_GUI.git">💻 GitHub</a>
</p>

<p align="center">
  🏆 <b>#1 Open-Source End-to-End Model on OSWorld (15 steps)</b>: Achieves <b>32.13%</b> success rate<br>
  📊 <b>Extreme Data Efficiency</b>: Matches GUI-OWL-7B performance using only <b>128 training tasks</b>
</p>

## Model Description

**BEPA-7B-S2** is a GUI agent model fine-tuned from [UI-TARS-1.5-7B](https://huggingface.co/bytedance-research/UI-TARS-1.5-7B) using the BEPA (Bi-Level Expert-to-Policy Assimilation) framework. This model achieves state-of-the-art performance among open-source end-to-end models on the OSWorld benchmark.

### Key Results

| Method | D<sub>expert_only</sub> | D<sub>train</sub> | D<sub>held_out</sub> | Overall (%) |
|--------|-------------------------|-------------------|----------------------|-------------|
| UITARS1.5-7B | 18.52 | 55.12 | 5.74 | 22.87 |
| GRPO | 11.11 | 58.02 | 5.32 | 23.60 |
| **BEPA (ours)** | **35.19** | **73.23** | **10.30** | **32.13** |

BEPA improves UI-TARS-1.5-7B from **22.87%** to **32.13%** on OSWorld-Verified (+9.26 points, +40.5% relative improvement).

## BEPA Framework

<p align="center">
  <img src="https://leon-gittech.github.io/Verl_GUI/stats/overview.png" alt="BEPA Overview" width="90%">
</p>

BEPA addresses two key challenges when using expert trajectories for training end-to-end GUI policies:

1. **Structural Mismatch:** Framework traces interleave multiple roles (planning, execution, grounding) that end-to-end policies cannot directly imitate.
2. **Distribution Gap:** Even after format conversion, trajectories remain far from the base-policy manifold.

### LEVEL-1: Self-Rolled Execution
Transforms alien expert traces into policy-compatible trajectories by abstracting expert trajectories into compact natural-language plans, then letting the base policy act in the environment with plan conditioning.

### LEVEL-2: Self-Aligned Assimilation
Dynamically maintains a per-task cache, injecting guided trajectories into GRPO updates only upon total on-policy failure. The cache is continuously refreshed with the policy's own successful executions.

## Citation

```bibtex
@misc{wang2026offpolicyonpolicyenhancinggui,
      title={From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation},
      author={Zezhou Wang and Ziyun Zhang and Xiaoyi Zhang and Zhuzhong Qian and Yan Lu},
      year={2026},
      eprint={2601.05787},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.05787},
}
```


## License

This model is released under the [MIT License](https://opensource.org/licenses/MIT).

## Acknowledgements

- [veRL](https://github.com/volcengine/verl) for the RL framework
- [vLLM](https://github.com/vllm-project/vllm) for fast inference
- [OSWorld](https://github.com/xlang-ai/OSWorld) for the benchmark
- [UI-TARS](https://github.com/bytedance/UI-TARS) for the base model