LLM Tokenization
The conversion of text or other input into model-specific token identifiers that a large language model can process.
Tokenization affects context usage, latency, cost, multilingual behavior, input limits and how reliably specialized terms, code or identifiers are represented.
Before sending long documents to an LLM, a pipeline counts tokens with the model's own tokenizer, preserves required instructions and chunks source text within the context limit.
Tokens are not equivalent to words or meaning, and counts vary by model and language. Truncation can remove critical context, while poor segmentation can disadvantage scripts, domains or uncommon strings.
A tokenizer normalizes and segments input into words, subwords, characters or bytes, maps segments to vocabulary IDs and adds special tokens. Padding and truncation shape batches; decoding maps generated IDs back to text.
AI Governance, Data Engineering, Systems Architecture
Hugging Face Transformers — Tokenization algorithms — https://huggingface.co/docs/transformers/tokenizer_summary; Hugging Face Transformers — Tokenizers — https://huggingface.co/docs/transformers/fast_tokenizers