A study shows that tailoring the vocabulary of small language models (SLMs) can cut inference cost by 77% in real applications. The paper details how pruning and custom tokenization reduce token counts while preserving accuracy, offering a practical path to cheaper, faster deployment for developers.
Opening Kapyn…