Generate_Theater

A minimal introduction to building and training a small-scale Large Language Model (LLM) from scratch.

Overview

This project demonstrates the foundational concepts behind LLMs by training a character-level language model on a Shakespeare text dataset. The model learns to generate new text in the style of Shakespearean English.

Based on the work of Andrej Karpathy, this implementation serves as a hands-on tutorial for understanding:

• How neural networks can learn patterns in sequential data
• The basic architecture of a transformer decoder
• The training process for generative text models

Purpose

This was created as a brief educational introduction to LLMs—covering core concepts like tokenization, attention mechanisms, and text generation—without the complexity of production-scale models.

Project Structure

• input.txt: Shakespeare text dataset for training
• gpt_dev.ipynb: Development notebook for experimentation
• gpt.py: Transformer-based model implementation
• bigram.py: Basic bigram language model implementation

Getting Started

Install required packages:

pip install torch

Note

This is a simplified, educational implementation meant to illustrate LLM fundamentals. Real-world LLMs are significantly larger, more complex, and trained on vastly more data.