Phase 3: LLM Architecture · ~90 minutes · Python
Tokenizers: BPE, WordPiece, SentencePiece
Your LLM does not read English. It reads integers. The tokenizer decides whether those integers carry meaning or waste it.
Hiring signal: Understanding of tokenizers: bpe, wordpiece, sentencepiece internals
What you will learn
- Implement BPE, WordPiece, and Unigram tokenization algorithms from scratch and compare their merge strategies
- Explain how vocabulary size affects model efficiency: too small creates long sequences, too large wastes embedding parameters
- Analyze tokenization artifacts across languages and code, identifying where specific tokenizers break down
- Use the tiktoken and sentencepiece libraries to tokenize text and inspect the resulting token IDs
Introduction
Type: Build Languages: Python Prerequisites: Phase 05 (NLP Foundations) Time: ~90 minutes
Learning Objectives
- Implement BPE, WordPiece, and Unigram tokenization algorithms from scratch and compare their merge strategies
- Explain how vocabulary size affects model efficiency: too small creates long sequences, too large wastes embedding parameters
- Analyze tokenization artifacts across languages and code, identifying where specific tokenizers break down
- Use the tiktoken and sentencepiece libraries to tokenize text and inspect the resulting token IDs
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