Llama2 Fine-Tuning

Welcome to LlamaTune, a dedicated repository for fine-tuning Llama2 model for advanced AI tasks. Our goal is to enhance the capabilities of pre-trained Llama models, tailoring them to specific datasets and use cases.

Features

• Preprocessing scripts for preparing your data.
• Fine-tuning pipelines for Llama models.
• Evaluation scripts for assessing model performance.
• Comprehensive documentation for easy replication and customization.

Getting Started

Prerequisites

Ensure you have Python 3.9+ installed on your system.

Installation

Clone the repository:

git clone https://github.com/soheilmohammadpour/LlamaTune.git
cd LlamaTune

Install dependencies:

pip install -r requirements.txt

Usage

Refer to our fine-tuning guide and usage examples for detailed instructions on how to use this repository.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

License

Distributed under the MIT License. See LICENSE for more information.

File Structure

The file structure for this repo is as follows.

LlamaTune/
│
├── config/                      # Configuration files
│   ├── model_config.py          # Model configurations
│   └── training_config.py       # Training configurations
│
├── data/                        # Data handling
│   ├── raw/                     # Raw datasets
│   ├── processed/               # Processed datasets
│   ├── preprocess.py            # Script for preprocessing data
│   └── load_data.py             # Script for loading datasets
│
├── models/                      # Model-related files
│   ├── model_utils.py           # Model loading and saving utilities
│   ├── evaluate.py              # Evaluation script
│   └── train.py                 # Training script
│
├── tokenizers/                  # Tokenizer-related files
│   └── tokenizer_utils.py       # Tokenizer utilities
│
├── utils/                       # Utility functions
│   └── general_utils.py         # General utilities
│
├── notebooks/                     # Jupyter notebooks for demos and tutorials
│   └── fine_tuning_examples.ipynb # Example notebook
│
├── tests/                       # Test scripts
│   ├── test_preprocess.py       # Tests for data preprocessing
│   ├── test_model.py            # Tests for model functionality
│   ├── test_tokenizer.py        # Tests for tokenizer functionality
│   └── test_utils.py            # Tests for utility functions
│
├── generation/                  # Scripts for response generation
│   └── generate.py              # Response generation script
│
├── README.md                    # Project README
├── setup.py
└── requirements.txt             # Project dependencies