Let's build the GPT Tokenizer
Andrej Karpathy
Feb 20, 2024
Tokenization is not merely a preprocessing step; it is the fundamental unit of LLM reasoning. Inefficient tokenization—like that found in early models—bloats sequences and cripples performance on tasks like coding and non-English languages. Mastering Byte Pair Encoding (BPE) is essential to optimizing context length and model efficiency.
Key insight: The 'Solid Gold Magikarp' phenomenon and erratic performance on simple arithmetic or Python indentation are often not failures of the neural network architecture, but direct consequences of poor tokenization choices.
