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Vocabulary Size

The number of distinct tokens a language model can process, typically 30K-100K+ tokens.

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2 stops · basics first
  1. Token ✓ understood

    The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.

  2. Tokenization ✓ understood

    Splitting text into tokens, usually subword pieces, and mapping each to an integer ID so a language model can process it.

  3. Vocabulary Size · you are here ✓ understood

The number of distinct tokens a language model can process, typically 30K-100K+ tokens.

This concept is essential for understanding large language models and forms a key part of modern AI systems.

  • Tokenization
  • Token
  • Embedding

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Vocabulary Size

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Language & LLMs Subword Tokenization Breaking words into smaller units, balancing vocabulary size with representation granularity. Language & LLMs BPE Byte Pair Encoding - a subword tokenization algorithm that iteratively merges frequent character pairs to create a vocabulary. Language & LLMs Embedding A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together. Language & LLMs Token The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.