Automation with Laya

Java port of Laya with choice/score/noul, a script-detecting router that picks the checkpoint, HuggingFace Hub fetching and bit-level parity with the Python ONNX output.

Tokenizer, mmBERT-base encoder and decision head in one binary for the 322M multilingual checkpoint. No Python, torch or BLAS; forward pass checked against a reference.

AXERA-TECH's conversion of all three Laya checkpoints for its edge AI chips, reported at latency under 70 ms per decision on AX650/AX8850.

Native Swift library on Apple MLX. Call prepare once to download ~804 MiB of weights, then predict with choice questions straight from your app.

One simulated Swedish villa, four controllers on the same minutes. Laya with a safety clip cut the bill from 985 to 530 SEK at p95 179 ms; a plain tariff EMS reached 385 SEK.

TypeScript library where one set of question definitions serves a zero-dependency Promise client and an Effect service. Never invents a probability it was not given.

SwiftPM package ported from laya-coreml. Point it at the aac6fef Core ML bundles (general or ANE) and call predict with choice, score or noul; no Python at runtime.

Nandakishor’s dev.to write-up on building decision models a year before Jev, RLCD training against proper scoring rules, and why Laya is open.

ONNX export of the Laya checkpoint so it runs anywhere onnxruntime does, including browsers and edge boxes. An fp16 variant followed a day later.