Laya is Convai Innovations' decision engine on ModernBERT-large with 421M parameters. A 322M multilingual sibling is also available.
By Convai Innovations · Released 18 Sep 2026
A non-autoregressive decision engine on ModernBERT-large (421M), with a 322M multilingual sibling. Its author, Nandakishor Mukkunnoth, says he built the same approach a year before Jev launched.
Source: Site → Source: GitHub →
A local inspection workbench: OpenCV measures the image, hard rules decide anything numeric, and a Laya decision model cross-checks the result. Built for fruit picking, weed and pest monitoring, and steel pipe and textile checks. Version 3.0 also trains your own Laya-format decision models.
A native DuckDB extension that adds seven Jev-style SQL functions backed by a local Laya model: DuckDB handles geometry and distance, Laya reads the text fields. It uses no hosted model or API keys.
Runs Bespoke Nimble-9B and three Laya checkpoints as players over the same 200 seeds of a headless Slay the Spire simulator, zero-shot and fine-tuned. The results are the authors' own.
Classifies Apple Mail messages as for you, to review or junk with a Laya model running on your own computer, and shows a report of what it would do. Mail never goes to an external AI service, and it never deletes anything.
Routes support messages through regex, then a small classifier, then decision models, then an LLM as a last resort, all running locally. Laya answers the guardrail and department questions, and Tev1 is the default for the next tier.
Tags each pet product's species, life stage and allergens from the store's own text, asking Jev or Laya one typed question per value: supported, contradicted, unknown or conflicting, with the source sentence attached. Merchants can approve, correct and lock tags, or let a validated policy accept them automatically. A hackathon entry for AIBoomi Startup Weekend Kochi, 23-24 Oct.
An MCP server that gives an AI assistant yes/no, choice and score tools backed by whichever decision model you configure: any /v1/systemone endpoint such as Jev or Clef, or Laya running on Apple silicon. Its tools never name a model family.
A small ticket router: with the Laya backend selected, each support ticket goes to Laya, the open Jev-style model, with three questions (which team, how urgent, is it spam). The default backend is a keyword stub, with a slot left for Jev.
Simulates a moderation queue hit by a wave of privacy-invading posts the existing router did not prioritise: Claude reads 20 of those posts and 20 ordinary ones and drafts one yes/no question, and a fine-tuned version of the open decision model Laya asks it of every new post so the matches jump to the urgent queue.
jevgrep finds code by asking Jev thousands of yes/no questions. layagrep records those questions on SWE-bench tasks and fine-tunes the open Laya to answer them locally, with no API key and no code leaving the machine. A smoke run on 400 held-out questions lifted file-navigation AUC from 0.79 to 0.94 (Jev 0.97), but declaration relevance stayed at 0.58 (Jev 1.00).
A desktop notes app for Mac, Windows and Linux that keeps plain Markdown files and runs the open Laya model on your own computer. Each tag is a yes/no question, like "Is this note about a trip?"; Laya answers all of them for a note in one pass and keeps the tags it is confident about. Notes stay on the computer; the only network request is a one-time download of about 1.6 GB of model files.
Runs multilingual Laya through a local Go/ONNX service, with a ZCode plugin for risk checks, context selection and 13 MCP tools. VENDOR-REPORTED on an RTX 3060, three runs per variant: total input fell from 359,085 to 239,544 tokens (33.3%) and requests from 16 to 9, but non-cached input rose 21.8%. Laya ranking alone cut total input 9.3% and non-cached input about 2.8%, within the observed range; the author has not established a fresh-token or cost saving. The /decide endpoint took about 26 ms at p50.
Checks coding-agent tool calls with rules and a local multilingual Laya fine-tune, returning allow, review or block. VENDOR-REPORTED, base to fine-tune: the model alone held 32 then 39 of 43 attacks and allowed 25 then 44 of 50 everyday commands. Six end-to-end verdicts improved and two worsened, missing a preset no-regression criterion. Accuracy on 162 held-out training rows rose from 0.611 to 0.852. The author inspected the benchmark and evaluation sets between training runs, so they are less independent than a fresh test.
A local laya-multilingual fine-tune labels 9,696 Bilibili comments about Phantom Blade Zero with 14 questions and a two-level topic tree. The author reports 60.8 minutes of training on an RTX 3060. On 771 test comments with 1,485 decisions, topic accuracy rises from 0.210 to 0.911 and validity from 0.253 to 0.971. Test labels come from rules, whose topic precision is estimated at 72% to 75%, so 0.911 measures agreement with the rules. The README also discloses some train/test overlap. Toxicity detection fails at macro-F1 0.489, with only 11 training positives.
Authors' published runs · VENDOR-REPORTED; not independently reproduced