LLM Tokenizers Explained - BPE, SentencePiece, Pretrained vs Custom - Lecture 3

Learn how to implement and integrate production-level tokenizers into large language models through this comprehensive 42-minute tutorial. Upgrade from manual tokenization to real-world tokenization systems by exploring multiple tokenization approaches including BPE (Byte Pair Encoding), SentencePiece, and pretrained tokenizers from popular models like GPT-2, BERT, LLaMA, and T5. Discover how tokenizers convert text into tokens and numbers, understand the impact of vocabulary size and domain-specific text on tokenization, and master the process of training custom tokenizers from your own datasets. Work hands-on with essential libraries including sentencepiece for custom tokenizer training, the tokenizers library for BPE implementation, gensim for Word2Vec and FastText embeddings, and transformers for HuggingFace tokenizers. Explore how embedding layers convert token IDs into vectors and learn to integrate these tokenization systems into a TinyGPT model built from scratch. Access the complete code implementation through the provided GitHub repositories and follow along with practical examples that demonstrate the differences between various tokenization approaches and their real-world applications in LLM development.