Let's build the GPT Tokenizer

Executive Summary

This comprehensive guide explores tokenization in Large Language Models (LLMs), a crucial yet complex component of natural language processing. The video covers the fundamental concepts, implementation details, and common challenges in tokenization, with specific focus on GPT models and industry-standard practices.

Key Topics Covered:

• Byte-Pair Encoding (BPE) algorithm implementation
• GPT-2 and GPT-4 tokenization differences
• Sentence Piece tokenizer analysis
• Common tokenization challenges and solutions
• Special tokens and vocabulary size considerations
• Token efficiency across different formats (JSON vs YAML)

Technical Highlights:

• Implementation of tokenization from scratch
• Analysis of OpenAI’s Tiktoken library
• Comparison of different tokenization approaches
• Vocabulary size optimization strategies
• Impact of tokenization on model performance

Common Issues Addressed:

• Non-English language processing challenges
• Arithmetic computation difficulties
• Python code handling improvements
• Special token handling issues
• The “Solid Gold Magikarp” phenomenon

Best Practices Recommended:

• Using GPT-4 tokenizer when possible
• Careful consideration of vocabulary size
• Proper handling of special tokens
• Understanding token efficiency in different formats
• Awareness of tokenization’s impact on model behavior

Keywords: tokenization, LLM, GPT, byte-pair encoding, natural language processing, machine learning, AI, OpenAI, Tiktoken, neural networks