Peek2: Regex-free Byte-level Byte-Pair Encoding Pretokenizer for LLM Inference on Edge Devices
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Abstract (arXiv)
Pretokenization is a crucial, sequential pass in Byte-level BPE tokenizers, yet little work has been done to optimize it for edge-side inference. Our proposed new implementation, Peek2, serves as a drop-in replacement for cl100k-like pretokenizers used in GPT-3, LLaMa-3, and Qwen-2.5. After breaking down and analyzing the logic of the original cl100k pretokenizer, we introduced a new pretokenization algorithm with linear time complexity and constant, trivial memory usage, suited for edge scenarios. Test results show that it increases microbenchmarking throughput by up to 2.48× and delivers a 1.14× improvement in overall throughput across the entire Byte-level BPE encoding process, depending on the dataset, while providing identical results as the baseline Regex-based tokenizer.
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References · 6
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
- 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
- 2024Tokenization Is More Than Compressioncited by 89
- 2025OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Trainingcited by 21
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