# What is Laya?

> Laya, from Convai Innovations: ModernBERT and mmBERT checkpoints, Router mode, RLCD training, typed questions, and limits from the model card.

## Answer

Laya is a non-autoregressive decision stack. You define questions and options. The model returns structured answers with probabilities in one pass.

Convai's open decision family: one forward pass, Choice / Score / Noul, Apache 2.0. Not a tiny chatbot wearing a trench coat.

## Publisher and license

Laya is released by Convai Innovations under Apache 2.0 on Hugging Face (convaiinnovations/laya). The upstream SDK and training code live at github.com/NandhaKishorM/laya with a PyPI package named laya. This directory is independent. We do not represent Convai.

## Checkpoints in the family

Three public checkpoints share one hub repo: the English root model (ModernBERT-large, about 421M params, 512 context) for English guardrails and email-style triage; laya-multilingual (mmBERT-base, about 322M, 1024 context, up to 8k encoder RoPE) for 100+ languages; and laya-typed-decisions (ModernBERT-large, 1024 context) fine-tuned for four workflow-style task groups Convai documents on the card. Only the subfolder you request downloads.

## Primitives: choice, score, noul

Questions are declared as JSON. Choice picks among criteria keys with softmax over option markers. Score returns an ordinal rating. Noul returns a calibrated probability for a yes/no style statement. This mirrors the TypeSafe Choice, Score, and Noul vocabulary in /learn/jev-typesafe. Useful for mental mapping, not a promise of API compatibility.

## Router mode

The recommended Python entrypoint is laya.Router. It detects script and language in sub-millisecond Python and routes to English or multilingual checkpoints. Convai documents large accuracy gaps when English-only weights see non-Latin scripts, so routing is a safety feature, not just convenience. Preload checkpoints in memory to avoid multi-second cold rebuilds when languages alternate.

## Training objective

Convai describes RLCD (reinforcement learning for calibrated decisions). The policy is rewarded with strictly proper scoring rules so honest probabilities maximize expected reward. That is why teams want System One models instead of parsing chat text. You still need domain temperature fitting on your data before you trust thresholds.

## Honest limits (from the model card)

Base English and multilingual checkpoints are weak on Convai's typed-decisions benchmark without fine-tuning. The 0.766 accuracy figure belongs to laya-typed-decisions, not the root weights. High-cardinality choice questions can exhaust per-option token budgets unless you raise head_max_len or split hierarchically. Ordinal score tasks are weaker than choice/noul on some public sets. Do not run English-only root for non-English production without multilingual or routed paths.

## In the directory

- [Laya vs Jev hub](https://jev.aitools.fyi/learn/laya-vs-jev)
- [Laya vs Jev compared](https://jev.aitools.fyi/learn/laya-vs-jev/compare)
- [System One primer](https://jev.aitools.fyi/learn/system-one)
- [What Jev is](https://jev.aitools.fyi/learn/jev-typesafe)
- [Benchmarks](https://jev.aitools.fyi/categories/benchmarks)
- [SDKs and clients](https://jev.aitools.fyi/categories/sdks)
- [@receptron/laya](https://jev.aitools.fyi/tools/receptron-laya)
- [ollaya](https://jev.aitools.fyi/tools/ollaya-dev-ollaya)
- [Laya-CoreML](https://jev.aitools.fyi/tools/mizorewww-laya-coreml)

## FAQ

### Is Laya generative AI?

No. It does not continue text token by token. Outputs are discrete decisions and probabilities over your schema.

### How do I install it?

pip install laya, then laya.load() or Router(preload=True) per the Hugging Face quickstart. Set USE_TF=0 if Transformers TensorFlow probing hangs your environment.

### Is Laya the same as Jev?

Same product category (typed parallel decisions), different vendor, license, and hosting model. Compare in /learn/laya-vs-jev/compare.

## Sources

Directory copy checked 2026-09-30. Primary sources:

- [Hugging Face: convaiinnovations/laya (model card and benchmarks)](https://huggingface.co/convaiinnovations/laya?ref=jev.aitools.fyi)
- [GitHub: NandhaKishorM/laya (upstream SDK)](https://github.com/NandhaKishorM/laya?ref=jev.aitools.fyi)
- [PyPI: laya package](https://pypi.org/project/laya/?ref=jev.aitools.fyi)
- [TypeSafe docs: System One / Jev API](https://docs.typesafe.ai/?ref=jev.aitools.fyi)
- [Laya-MLX: BENCHMARKS.md (M3 Max, independent port)](https://github.com/mizorewww/laya-mlx/blob/main/BENCHMARKS.md?ref=jev.aitools.fyi)

Canonical: https://jev.aitools.fyi/learn/laya-vs-jev/what-is-laya
Markdown: https://jev.aitools.fyi/learn/laya-vs-jev/what-is-laya.md