# Overview

The implementation of our method FPVE on Task 3 is based on the prior work FASP ([GitHub - chandar-lab/FASP: We study the effect of attention head pruning on fairness in large language models](https://github.com/chandar-lab/FASP)), which supports both weighted sum strategy and multi-objective evolution techniques.

## Workflow

To use our proposed method F-PvE:

```bash
bash run_FPVE.sh
```

Usage instructions can be found in run_FPVE.sh and args.py.

To use the baseline method FASP:

```bash
bash run_FASP.sh
```

## Configuration

- Unzip the transformers_pruning.zip file under the FASP directory
- Adjust pruning parameters in  `run_FPVE.sh` based on your requirements
- Set the correct path for the model and data.
