GPT-4 Experiments

Overview

This repository contains experiments with OpenAI's GPT-4 model. It includes scripts and tools designed to handle data, run experiments, and benchmark the performance of GPT-4 across various tasks.

Repository Structure

• benchmarks/: Contains benchmark datasets and results used to evaluate GPT-4's performance.
• data_handler.py: Manages data loading, preprocessing, and augmentation.
• local_functions.py: Houses utility functions to support experiments.
• run_experiments.py: The primary script to execute experiments.
• LICENSE: Specifies the repository's licensing terms.
• README.md: Provides an overview and instructions for the repository.

Installation

To set up the environment for these experiments:

1. Clone the repository:

git clone https://github.com/benlonnqvist/gpt-4_experiments.git
cd gpt-4_experiments

2. Create a virtual environment:

python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Note: Ensure that requirements.txt is present in the repository with all necessary dependencies listed.

Usage

Data Preparation

Place your datasets in the benchmarks/ directory. Ensure they are in the expected format required by data_handler.py. Benchmarks typically should contain some metadata in .json files. Follow the format of extant benchmarks that are provided.

Running Experiments

To initiate experiments, execute the run_experiments.py script:

python run_experiments.py

This script uses functions from data_handler.py and local_functions.py to process data and run experiments.

Viewing Results

Results and logs will be stored in the benchmarks/ directory. Review these files to analyze GPT-4's performance.

Contributing

Contributions are welcome! If you have ideas for improvements or new experiments, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/my-feature).
3. Make your changes and commit them (git commit -am 'Add new feature').
4. Push your changes to the branch (git push origin feature/my-feature).
5. Submit a pull request with a clear description of your changes.

Ensure that your code adheres to standard Python coding conventions and is well-documented.

License

This project is licensed under the MIT License. See the LICENSE file for more details.