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TIKTOKEN
Fast BPE tokenization for LLMs
MIT
ABOUT
LLM applications need to count tokens accurately for prompt construction, context window management, and cost estimation — but tokenization is encoding-specific and varies by model family. Most tokenizers are too slow for real-time applications or require loading model weights just to tokenize text. Tiktoken provides a fast, lightweight BPE tokenizer implemented in Rust with Python bindings that supports all major OpenAI encoding formats and many third-party models, handling 10x the throughput of slower alternatives.
INSTALL
pip install tiktokenINTEGRATION GUIDE
1. Count tokens in prompts and responses to stay within model context windows
2. Estimate API costs by computing token usage before sending requests
3. Split long documents into token-aligned chunks for RAG retrieval
4. Build token-aware streaming interfaces that respect per-minute rate limits
5. Validate prompt length during autocomplete and multi-turn conversations
TAGS
tokenizerbpellmopenaitext-processingencoding