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TOKENIZERS
Fast subword tokenization for modern LLMs
Apache-2.0
ABOUT
Tokenization is the first step in any LLM pipeline — converting raw text into integer token IDs — but naive Python implementations are painfully slow for production workloads. Training a BPE vocabulary on a large corpus can take hours, and tokenizing at inference time becomes a bottleneck. Tokenizers solves this with a Rust core that tokenizes a gigabyte of text in under 20 seconds on a single CPU. It supports all major algorithms (BPE, WordPiece, Unigram) with built-in pre-processing (truncation, padding, special tokens) and alignment tracking that maps tokens back to their original characters. It's the engine behind virtually every model on Hugging Face Hub.
INSTALL
pip install tokenizers
INTEGRATION GUIDE
1. Train a BPE tokenizer on a multilingual corpus for a custom LLM in minutes instead of hours
2. Tokenize billions of tokens for a production LLM serving pipeline with sub-millisecond latency
3. Convert between tokenizer formats (WordPiece → BPE) when switching model architectures
4. Add padding and truncation to batches automatically for efficient GPU utilization
5. Track token-to-character alignment for span-level tasks like NER and question answering
TAGS
tokenizationnlphuggingfacesubwordbperustwordpiecellm