Laya

Quadrant scores

See the full quadrant

Scored with the public quadrant rubric (maturity × capability; bubble = adoption). Revise as evidence lands.

• Maturity6/10

  Apache weights plus PyPI / HF / Router packaging and clear docs; self-host only (no hosted SLA) caps the top of the maturity band.

• Capability6.8/10

  Full typed primitives and fast self-reported latency; needs temperature refit for calib, and zero-shot typed-decisions remain near chance without fine-tuning.

• Adoption12/100

  Some open-source and social mention vs Jev, but still obscure to most practitioners a few days after launch.

Vendor claims

• ~33–40ms per single question on T4 (author self-reported)[Vendor claim — not independently verified]
• typed-decisions fine-tuned checkpoint accuracy ~0.77 (author split)[Vendor claim — not independently verified]
• zero-shot typed-decisions ~0.36 (near chance; below majority ~0.46)[Vendor claim — not independently verified]

Laya is a full open System One–style decision model — ModernBERT-large fine-tuned with a decision head (~421M parameters), Apache-2.0 — not a LoRA adapter and not a TypeSafe product.

You load a checkpoint, pass shared state plus typed questions (choice, score, noul → catalog boolean), and get answers with probabilities in one forward pass. Packaging: PyPI laya, Hugging Face convaiinnovations/laya, source github.com/NandhaKishorM/laya. Family includes English, multilingual (mmBERT-base ~322M), and typed-decisions variants.

Limits (read before you ship)

• Zero-shot typed-decisions is weak (~0.36) — near chance; the ~0.77 figure is a checkpoint fine-tuned on that benchmark split, not generic zero-shot System One transfer.
• Overconfident until temperature refit — author reports ECE improving a lot after fitting temperature on your data (card: ~0.466 → ~0.081). Do not ship raw probs as calibrated without your own fit.
• Comps vs Jev on the card use different samples — Jev was not measured by the Laya authors on the same suite; any “above Jev” claim is not independently verified here.
• Choice option budget is limited (author: ≲ ~20 options by token budget).
• Independent lab note (unverified): @michhachula (19 Sep 2026) ran Laya locally (~29 ms / short question on M4 Pro) inside a 38-model router: passed 1/5 hard constraints; reversing option order changed 15/16 picks; wrong picks at >98% confidence; ~192-token question budget trimmed model details. Self-reported — not a ModelSystem.One eval. Reinforces: do not treat as drop-in Jev without your own suite.
• Latency ~33–40ms / 1q on T4 is self-reported; CPU / other GPUs will differ.

Community framing (unverified)

A Brazilian community post framed Laya as “open source do Jev / already above it” (@0xCVYH, 19 Sep 2026). Treat that as UnverifiedClaim / marketing awareness, not a ModelSystem.One benchmark. Useful only as an adoption signal (~60k impressions on that post).

Why it is in the catalog

Public typed decision I/O, open weights + PyPI docs, and a complete encoder+head model (not LoRA). It sits next to Jev (hosted waitlist) and Kev (open LoRA prototype) as the stronger open full-model point on the quadrant — with the caveats above on zero-shot and calibration.