Tokenization Tutorial 🔢: Text to Numbers Hack for LLM Smarts

Master tokenization guide – the bottleneck for AI language models. Turn text into tokens efficiently.

Why Tokenization Matters in LLMs

• Arithmetic fails? Blame poor token splits.
• String manipulations? Depends on smart tokenization.

Tokenization Pipeline

1. Normalize: Clean and standardize text.
2. Pre-tokenize: Split into words or subwords.
3. Model: Apply BPE explained or similar to get IDs.
4. Post-process: Add specials like [BOS], [EOS].

Tokenization Paradigms

• Character-level: Universal but leads to long sequences.
• Word-level: Semantic but handles OOV poorly.
• Subword: BPE balance for efficiency.
• Byte-level: Raw UTF-8 for multilingual support.

BPE Steps Explained

• Initialize with characters.
• Count pair frequencies.
• Merge most frequent pairs.
• Repeat until vocabulary size reached.

My Tokenization Notes

• Medium: Token Theory
• Colab: Tokenization Techniques

Top Tokenization Resources

• Mistral Tokenization Guide
• Hugging Face Pipeline
• Hugging Face Tokenizer Playground
• OpenAI Tokenizer Tool
• Airbyte LLM Tokenization Guide
• Tiktokenizer
• MIT Paper on Tokenization
• Medium: BPE Tutorial
• BPE Original Paper
• Karpathy Video on Tokenization
• MinBPE GitHub Repo

What’s your trick for handling rare words in tokenization? Share! 🤓

Keywords: tokenization tutorial, BPE explained, LLM tokenization guide, text to tokens, subword tokenization