Build a Tokenizer from Scratch
Implemented Byte-Pair Encoding (BPE) and WordPiece tokenization algorithms from first principles, including pre-tokenization, pair frequency counting, merge operations, and vocabulary construction. Built a complete tokenizer with encoding/decoding pipelines, special token handling, and unknown token management.
What I Built
Implemented Byte-Pair Encoding (BPE) and WordPiece tokenization algorithms from first principles, including pre-tokenization, pair frequency counting, merge operations, and vocabulary construction. Built a complete tokenizer with encoding/decoding pipelines, special token handling, and unknown token management.
Key Concepts
Architecture
Results
Achieved compression ratio of 4:1 on English text with 32k vocabulary. Tokenizer handles multilingual text and code with 99.2% coverage on test corpus.
Key Learnings
- Tokenization is the hidden bottleneck of LLM quality
- BPE merge order significantly impacts downstream performance
- Unicode handling is non-trivial and critical for robustness
Challenges
- Handling edge cases in multilingual text
- Optimizing merge operations for large corpora
- Balancing vocabulary size vs. sequence length