Results What V1 Is The Split: Bitstreams Instead Of A Regex The Word Cache The Merge Loop Method What This Adds Up To Getting It Progress Towards V1 Release Candidate: Implemented 1.0.0 After 1.0.0 The tokenizer has not historically been the bottleneck within ML workflows. Compute-wise, tokenization is light compared to the heavy modeling happening in the rest of the pipeline. Yet, in some cases, it has rapidly become key to accelerating (or slowing down) your machine learning work.
As models become faster and workloads scale, that balance begins to shift. Training on massive datasets, serving many concurrent requests, or repeatedly processing long inputs can put enough pressure on the tokenizer that it starves the model of data.
