Project Overview:

This project aims to gain a comprehensive understanding of Large Language Models (LLMs) by implementing a transformer-based model from scratch. It serves as an educational exploration of the architecture, components, and intricacies of LLMs, providing hands-on experience in designing, coding, and optimizing these models.

Key Objectives:

1.Understand Transformer Architecture:

Explore the encoder-decoder structure, including its critical components.

2.Implement the model in a modular fashion to:

Enhance code readability and maintainability. Allow for easy debugging and further development.

3.Optimization with Parallel Computing:

Leverage parallel computing with SparkX to enhance model efficiency during the early stages of training and testing.

The project serves as a learning tool, providing insights into the structure and mechanics of LLMs.