Phase 3: LLM Architecture · ~90 minutes · Python
Building a Tokenizer from Scratch
Lesson 01 gave you a toy. This lesson gives you a weapon.
Hiring signal: Understanding of building a tokenizer from scratch internals
What you will learn
- Build a production-grade BPE tokenizer that handles Unicode, whitespace normalization, and special tokens
- Implement byte-level fallback so the tokenizer can encode any input (including emoji, CJK, and code) without unknown tokens
- Add pre-tokenization regex patterns that split text at word boundaries before applying BPE merges
- Train a custom tokenizer on a corpus and evaluate its compression ratio against tiktoken on multilingual text
Introduction
Type: Build Languages: Python Prerequisites: Phase 10, Lesson 01 (Tokenizers: BPE, WordPiece, SentencePiece) Time: ~90 minutes
Learning Objectives
- Build a production-grade BPE tokenizer that handles Unicode, whitespace normalization, and special tokens
- Implement byte-level fallback so the tokenizer can encode any input (including emoji, CJK, and code) without unknown tokens
- Add pre-tokenization regex patterns that split text at word boundaries before applying BPE merges
- Train a custom tokenizer on a corpus and evaluate its compression ratio against tiktoken on multilingual text
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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