A Developer’s Guide to Laya: Zero-Shot Decisions and Calibration

תקציר מקורי באנגליתIn this tutorial, we work with Laya , the open-source decision engine from Convai Innovations that became one of the most-starred machine-learning repositories of September 2026. Laya is a non-autoregressive System 1 model: instead of generating text, a 421-million-parameter encoder reads a piece of text and a set of typed questions, a choice between labels, a score on a scale, or a yes/no, and returns a probability for every option in a single forward pass with zero output tokens. Its pitch is speed and calibrated probabilities, the open answer to TypeSafe’s Jev. Rather than repeat the README’s examples, we put those promises to work on real labelled data with known answers, the banking domain of the CLINC150 intent dataset, and measure what a production router actually gets: