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: 2,244 Bytes

9d8b150

---
license: mit
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- gui-agent
- rlvr
- computer-use
---

# BEPA-7B-S2

This repository contains the weights for **BEPA-7B-S2**, an end-to-end screenshot-to-action policy for GUI agents. The model was introduced in the paper [From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation](https://huggingface.co/papers/2601.05787).

## Introduction

**BEPA** (Bi-Level Expert-to-Policy Assimilation) is a framework designed to enhance Vision-Language Models acting as computer-use agents (CUAs). It addresses the challenges of using static expert trajectories in reinforcement learning from verifiable rewards (RLVR) by turning them into policy-aligned guidance.

BEPA operates in two complementary stages:
- **LEVEL-1 (Self-Rolled Execution):** Transforms alien expert traces into policy-compatible trajectories by abstracting them into natural-language plans and letting the base policy execute them.
- **LEVEL-2 (Self-Aligned Assimilation):** Dynamically maintains a per-task cache that injects guided trajectories into training updates when on-policy failures occur.

On the OSWorld-Verified benchmark, BEPA improves the success rate of UITARS1.5-7B from 22.87% to **32.13%**, establishing it as a top-performing open-source end-to-end model.

## Resources
- **Paper:** [https://huggingface.co/papers/2601.05787](https://huggingface.co/papers/2601.05787)
- **Project Page:** [https://leon-gittech.github.io/Verl_GUI/](https://leon-gittech.github.io/Verl_GUI/)
- **GitHub Repository:** [https://github.com/LEON-gittech/Verl_GUI](https://github.com/LEON-gittech/Verl_GUI)

## Main Results

| Method | Overall Success (%) |
|--------|-------------|
| UITARS1.5-7B | 22.87 |
| GRPO | 23.60 |
| **BEPA (ours)** | **32.13** |

## 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}, 
}
```