Laya AI Model: How It Works, Run It Locally, and Evaluate It

Laya is an Apache 2.0-licensed, open-weight family of non-autoregressive decision models from Convai Innovations that runs locally and returns typed answers with probability distributions rather than generative text. It accepts an input state and structured questions for fixed-option classification (Choice), ordinal ratings (Score), or true/false probability estimation (Noul), making it suited to tasks such as ticket routing, urgency assessment, and cancellation-risk detection while leaving business policy and customer-facing explanations to separate systems. Available checkpoints include an English ModernBERT-based model, a multilingual mmBERT-based model, and a typed-decisions variant, with context limits and long-input reliability requiring task-specific testing. The article outlines local Python deployment through the Laya Router, advises defining clear label rubrics and representative held-out datasets, and emphasizes evaluation beyond raw accuracy through confusion matrices, calibration analysis, abstention thresholds, subgroup testing, and shadow-mode deployment. It also notes that operational decisions should account for latency, hardware, model versioning, human-review costs, and recovery procedures, and contrasts Laya’s controllable local deployment with hosted typed-decision services such as Jev.

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