Non-autoregressive System 1 decision model, fine-tuned on the typed-decisions workflows: agent-trace observability, customer service, invoice processing and security incidents. Part of the Laya family. 400 test cases, 2,000 decisions, measured on the official test split. +3.9 points over Jev's published 0.727, above the 0.735 teacher ceiling, with 2.4x better Brier and 1.6x better score MAE. Jev figures are third-party published, not measured here — there is no TypeSafe API access in this project, and sample sizes and prompts differ. Treat the comparison as indicative. Router will not select this checkpoint automatically unless you construct it with autotaskdetection=True — it is…
Open weights
apache-2.0
421M parameters
transformers
Multilingual, non-autoregressive System 1 decision model. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with mathematically calibrated probabilities in a single forward pass (~33 ms) across 100+ languages. Trained with reinforcement learning against strictly proper scoring rules (RLCD), so reporting honest probabilities is the only way to maximise reward. It never generates text, so there is nothing to parse and nothing to hallucinate. This repo holds all three checkpoints and is the hub for the family. The English checkpoint is at the repo root; the other two are bundled subfolders, and only the one you request is downloaded: pip install -U…
Open weights
apache-2.0
421M parameters
transformers
A fine-tuned Laya model (Convai Innovations, 421M, ModernBERT-large backbone) that answers one typed noul question: does a value correctly match its key label? (e.g. firstname = John → true, firstname = 1992 → false). Fine-tuned on the IBM KVP-10K dataset via the pre-parsed community mirror (OCR pre-extracted; no OCR step needed). This is the v2 (remediated) release: document-level 80/10/10 data partitioning, mixed-class frozen test set, calib-split temperature fitting, and a frozen category-stratified release gate. It supersedes the v1 release pre-remediation, historical artifact). Laya is a non-autoregressive decision model: you give it a state (text/dict) plus typed questions (choice…
Open weights
apache-2.0
421M parameters
laya
Laya-layout ModernBERT + MASK option head, RLCD fine-tune from convaiinnovations/laya. Checkpoint files: model.safetensors, rlagentconfig.json, tokenizer/, encoder/. Load with AFM-D: python -m afmd.de.eval --checkpoint --device cuda. See AFM-D / product docs. License follows the base Laya / ModernBERT stack.
Open weights
421M parameters
transformers
OpenJevX is an open-weight, non-autoregressive System One decision model specialized from Laya, which uses answerdotai/ModernBERT-large plus a dynamic typed-decision head. It accepts runtime-defined choice, score, and noul questions and returns calibrated probabilities in one forward pass. It is compatible with the TypeSafe Jev /v1/systemone request shape through the OpenJevX server. - CUDA p50 latency per five-question case: 22.8 ms The benchmark uses the untouched 400-case, 2,000-decision test split from LocalLLaMA/typed-decisions. Training uses only its 1,200-case train split. OpenJevX is a derivative of Laya by ConvAI Innovations and ModernBERT by Answer.AI and LightOn. Laya and…
Open weights
apache-2.0
421M parameters
laya
Laya v3 is an ultra-fast, local System-1 Decision Engine fine-tuned on top of ModernBERT-Large for Nextflow DSL2 and the nf-core bioinformatics ecosystem. It is designed to make zero-latency, high-accuracy architectural decisions in autonomous pipeline compilers (such as Codaris), resolving complex setup questions without burning LLM tokens. Evaluated across 40 realistic Nextflow architecture tasks
Open weights
mit
421M parameters
