SentencePiece tokenizes text directly from raw Unicode without language-specific pre-tokenization. Key innovation: Treats input as stream of characters, no pre-tokenization (like splitting on spaces) needed. Language-agnostic. Algorithms: Supports BPE and Unigram algorithms within the SentencePiece framework. Unigram mode: Starts with large vocabulary, prunes tokens that least reduce corpus likelihood. Maintains probability per token. Whitespace handling: Can represent spaces as special character (underscore), enabling lossless tokenization-detokenization. Multilingual benefit: Works uniformly across languages, critical for multilingual models. No language-specific preprocessing. Used by: T5, mT5, LLaMA, XLNet, ALBERT, and many multilingual models. Training: Learn vocabulary from corpus, produces model file for encoding. Inference: Probabilistic sampling possible (multiple tokenizations), deterministic mode for consistency. Command line tools: spm_train for training, spm_encode/spm_decode for usage. Comparison: More flexible than WordPiece, handles diverse scripts better. Standard choice for multilingual and open-source LLMs.