Let's Build GPT

Contents

Lesson 1: Building GPT From Scratch

Description:
A foundational lesson that walks through the core concepts of GPT architecture by implementing it from scratch. Perfect for those wanting to deeply understand transformer-based language models.

Key Topics:

• Basic neural network concepts
• Transformer architecture fundamentals
• Self-attention mechanisms
• Feed-forward networks
• Implementation in PyTorch
• Training process walkthrough

Lesson 2: Building a GPT Tokenizer

Description:
Learn how modern language models process and tokenize text. This lesson covers the implementation of a tokenizer similar to those used in production models.

Key Topics:

• Text preprocessing basics
• Byte-Pair Encoding (BPE)
• Vocabulary creation
• Token encoding/decoding
• Subword tokenization
• Handling special tokens
• Practical implementation

Lesson 3: Reproducing GPT-2 (124M)

Description:
Advanced lesson on scaling up the concepts from previous lessons to build a GPT-2 style model with 124M parameters.

Key Topics:

• Model scaling principles
• Parameter initialization
• Layer normalization
• Attention masking
• Optimization techniques
• Model training at scale
• Performance evaluation

Prerequisites

• Python programming experience
• Basic understanding of neural networks
• Familiarity with PyTorch
• Linear algebra fundamentals
• Basic probability and statistics

Learning Outcomes

After completing this series, you will:

• Understand transformer architecture in-depth
• Be able to implement basic language models
• Know how tokenization works in modern NLP
• Gain practical experience with PyTorch
• Understand scaling considerations for large models

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Note: This is an educational series focused on understanding the principles behind GPT-style models through hands-on implementation.