AI Models & Assistants / Knowledge library
Tokenization
Mapping text or other inputs into model token identifiers
Why learn Tokenization?
Tokenization gives a finite vocabulary coverage of open-ended text. Token boundaries affect context use, cost, multilingual behavior, and how strings such as code or numbers are represented.
Where it came from
Modern language models commonly use learned subword tokenizers rather than one token per word. A tokenizer defines the exact mapping between text, token IDs, and decoded text used by a particular model.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain tokenization: What does a language-model tokenizer produce from input text?
- Use the matching tokenizer: Why must inference normally use the tokenizer released with a model?
- Estimate context consumption: Two texts have the same character count but different token counts. What can differ as a result?
- Contrast tokenization strategies: What advantage do byte-level or byte-fallback tokenizers provide?
- Diagnose token-boundary effects: A model struggles to copy a rare identifier exactly. Which inspection is most directly useful?
Documentation and sources
- Hugging Face tokenizer summary (huggingface.co)