LLMs require tokenizers to convert text into numerical IDs before training or inference. This piece explains how Byte-Pair Encoding (BPE) and SentencePiece work, compares them across criteria like multilingual support, training cost, and whitespace handling, and provides working Python code examples for both. It also covers
Table of contents
Key TakeAwaysWhy Tokenization MattersHow LLM Tokenizers Work: BPE, SentencePiece, Pretrained vs CustomByte‑Pair EncodingTraining Tokenizers from Scratch: BPE vs SentencePiece ExplainedWhen Should You Train a New Tokenizer?Practical Example: Token Count ComparisonDomain Adaptation and CompressionFrequently Asked QuestionsConclusionReferences and ResourcesSort: