convaiinnovations/laya (multilingual, non-autoregressive System 1 decision model)

Laya is an open-weight (Apache 2.0) "System 1" decision model from Convai Innovations, built by Nandakishor M as an open alternative to TypeSafe's closed Jev API. Instead of generating text, it takes a state (text, an email, a ticket, or JSON) plus typed questions (choice to pick a label, score to place something on an ordinal rubric, and noul for a yes/no probability) and answers all of them in one forward pass in about 33–40 ms on a GPU, so there's no output to parse and nothing to hallucinate.

It comes in three checkpoints: a 421M-parameter English model on ModernBERT-large, a faster 322M multilingual model on mmBERT-base covering 100+ languages, and a variant fine-tuned for typed-decisions workflows. A built-in Router detects the input's script and sends it to the right checkpoint. It's trained with RLCD, a reinforcement learning method whose reward uses strictly proper scoring rules, so the model maximizes reward only by reporting honest probabilities; it also has an act-vs-escalate head for deciding when to hand off to a human.

The author reports strong results, including beating Jev on AG News, emotion classification, and the typed-decisions benchmark while running roughly 6–8× faster, though the Jev figures are third-party numbers rather than head-to-head runs. The model card is also candid about its limits: the base checkpoints are near chance on typed-decisions without fine-tuning, accuracy drops sharply with 50+ options (Banking77: 0.425 vs. Jev's 0.870), ordinal scoring is its weakest question type, the English checkpoint fails on non-Latin scripts, and the models ship overconfident, so you need to fit a temperature on your own data before trusting the probabilities.