---
language:
- en
library_name: peft
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- vision
- agent
- browser-use
- sft
- imitation-learning
---

# Qwen2.5-VL-7B - Browser Agent (SFT Baseline)

This is a LoRA adapter for `Qwen2.5-VL-7B-Instruct` trained to act as a visual web browser agent. It was trained using **Imitation Learning (Behavioral Cloning)** on a dataset of expert human trajectories capturing screenshots, DOM elements, and browser actions.

## 🔬 Methodology
- **Training Strategy**: Supervised Fine-Tuning (SFT) over 15+ epochs.
- **Dataset**: Constructed via DAgger (Dataset Aggregation). We used live thought injection to guarantee reasoning-rich traces.
- **Goal**: Serve as the stable baseline for subsequent Reinforcement Learning (GRPO) experiments.

## 🚀 Performance
This model successfully navigates standardized benchmark tasks and outputs actions in the strict JSON format required by the `browser-use` library. However, it may struggle with negative constraints compared to the RL-refined version.

## 💻 Usage
```python
from peft import PeftModel
from transformers import Qwen2_5_VLForConditionalGeneration

model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", device_map="auto")
model = PeftModel.from_pretrained(model, "ihaveadog/qwen25-vl-7b-browser-agent-lora")
```