What is Tokenization? Tokenization is the process of turning text into small units called tokens that a Large Language Model (LLM) can process. - How It Works Text Splitting: Input text is broken down into units like whole words, sub-words, or individual punctuation marks. ID Assignment: Each unique token is mapped to a specific numerical ID in a massive pre-defined vocabulary lookup table. Special Tokens: Control tokens like [CLS] or <|endoftext|> are added to mark boundaries, context, or specific sequence instructions. - How LLMs Process Tokens LLMs convert token IDs into vector embeddings, process relationships using attention layers, and predict the numerical ID of the most likely next token. For example, you write this prompt: "What is tokenization in LLM?" An LLM doesn't directly process those characters as words. So, a tokenizer might split it roughly like: "What" | " is" | "tokeniza" | "tion" | "in" | "LLM" | "?" Now each token is then mapped to a number: "What" → 40 "is" → 3021 "tokeniza" → 5780 "tion" → 6975 "in" → 7976 "LLM" → 2995 "?" → 204 So the model actually receives something like: [40, 3021, 5780, 6975, 7976, 2995, 204] Those numbers are token IDs. The model converts each ID into an embedding (a vector of numbers) and processes those vectors with its transformer layers. Overal concept: Text ↓ Tokenizer ↓ Tokens ↓ Token IDs ↓ Embeddings ↓ Transformer ↓ Predicted next-token probabilities The key idea is: - LLMs don't read and write words. - They receive token IDs and repeatedly predict the next token. - Tokenization is the bridge between human-readable text and the numerical representation the neural network operates on. Bellow is a visual explanation If you liked it, share it with your network. Follow @codewithkhalil for more.