Let's build GPT: from scratch, in code, spelled out

Key Points

• The video demonstrates how to build a transformer model similar to GPT from scratch using PyTorch
• Covers implementation of key transformer components:
  • Self-attention mechanism
  • Multi-head attention
  • Positional encodings
  • Feed-forward networks
  • Layer normalization
  • Residual connections

Technical Implementation

• Built using Python and PyTorch
• Trained on tiny Shakespeare dataset
• Approximately 200 lines of code
• Achieved validation loss of 1.48
• Model size: ~10 million parameters

ChatGPT Development Process

1. Pre-training Stage

   • Training on large internet text corpus
   • GPT-3 uses 175 billion parameters
   • Trained on 300 billion tokens

2. Fine-tuning Stage

   • Alignment training with question-answer pairs
   • Reward model training
   • Policy optimization (PO)

Key Differences from ChatGPT

• Implemented decoder-only transformer
• Smaller scale implementation
• Character-level tokenization vs. GPT’s subword tokenization
• No fine-tuning or alignment training

Practical Applications

• Text generation
• Language modeling
• Understanding foundational AI concepts
• Educational purposes

This implementation provides a practical understanding of transformer architecture and serves as a foundation for understanding larger language models like GPT-3 and ChatGPT.