laya for Ollaya

Ollaya package of convaiinnovations/laya by Convai Innovations. Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API.

ollaya run laya

What is in this repository

This repository holds only the files Ollaya derives, with no weights. Each graph is an ONNX export of the original model whose weights reference the authors' own weight files by byte offset, so ollaya pull downloads the weights from the upstream repositories, unmodified and pinned to a commit, and verifies their sha256.

Tag	Upstream	Files
laya:en	convaiinnovations/laya@aa8c91c	en/model-fp32.onnx, en/model-fp16.onnx, en/decision.json, en/calibration.json
laya:multilingual	convaiinnovations/laya@aa8c91c	multilingual/model-fp32.onnx, multilingual/model-fp16.onnx, multilingual/decision.json, multilingual/calibration.json
laya:typed-decisions	convaiinnovations/laya@aa8c91c	typed-decisions/model-fp32.onnx, typed-decisions/model-fp16.onnx, typed-decisions/decision.json, typed-decisions/calibration.json

Each tag has an fp32 graph (CPU) and an fp16 graph (GPU). Each tag also has decision.json (sequence layout, special tokens) and calibration.json (temperatures).

Parity

The exports are checked against the PyTorch reference on 2,383 questions per checkpoint. The checks use typed-decisions plus multilingual and edge cases:

• fp32: the same decision on 100% of questions, with probabilities within 1.1e-4.
• fp16: the same decision on 99.1–99.6% of questions. Nearly all of the differences are near-ties between the top two options.

The fp32 graphs (CPU) are opset 23: attention runs as fused Attention nodes, which ONNX Runtime's CPU provider runs faster than the decomposed ops, so they need ONNX Runtime 1.23 or newer. The fp16 graphs (GPU) are opset 20.

License

Same as the upstream model (Apache-2.0). Ollaya itself is Apache-2.0.