Large Language Models

Overview

This project focuses on developing and experimenting with Large Language Models (LLMs). It aims to explore the capabilities and applications of LLMs in various natural language processing (NLP) tasks. The project will eventually be deployed using Streamlit to provide an interactive web interface for users.

Features

• Implementation of state-of-the-art LLMs
• Fine-tuning models on specific datasets
• Evaluation and comparison of different models
• Interactive web interface using Streamlit (to be added)
• Comprehensive documentation and tutorials (to be added)

Table of Contents

• Installation
• Usage
• Project Structure
• Contributing
• License
• Acknowledgements

Installation

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Clone the Repository

git clone https://github.com/Dev-Vis1/Large-Language-Models.git
cd Large-Language-Models

Install Dependencies

pip install -r requirements.txt

Usage

Running the Models

Currently, the project is under development. You can run the existing scripts for model training and evaluation:

python train_model.py --config config.yaml
python evaluate_model.py --model_path path/to/model

Streamlit Deployment

Streamlit deployment will be added soon. Stay tuned for updates!

Project Structure

Large-Language-Models/
├── data/                  # Data files and datasets
├── models/                # Pre-trained and fine-tuned models
├── notebooks/             # Jupyter notebooks for experiments
├── scripts/               # Python scripts for training and evaluation
├── streamlit/             # Streamlit app (to be added)
├── config.yaml            # Configuration file
├── requirements.txt       # List of dependencies
└── README.md              # Project README file

Contributing

Contributions are welcome! Please read the CONTRIBUTING.md file for guidelines on how to get involved.

Reporting Issues

If you encounter any issues, please report them in the issue tracker.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Inspired by the work of researchers and developers in the field of natural language processing.
• Thanks to the open-source community for providing valuable resources and tools.

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