Ask typed questions about any text or JSON and get calibrated answers in milliseconds. Private, open source, on your own hardware.

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ollaya · zsh

$ollaya run laya --preset triage \

"I was charged twice this month and want a refund."

Answers returned by the modelQuestionAnswerProbabilityintentrefund1.00is_urgentno0.87frustration1.59 / 3 clearly annoyed0.36refund_requestedyes0.88churn_riskno0.89$

Real output: routed to laya:en, answered in 8.9 ms on an RTX 4090.

Fast

Drop-in compatible

Open models

Your data stays yours

Platforms

Fast

Decisions in milliseconds.

A decision model answers in a single forward pass, with no token-by-token generation. On your own GPU, a five-question request to Laya takes about 10 ms, end to end through the HTTP API.

Ollaya: median of a five-question request through the HTTP API on an NVIDIA RTX 4090 (laya in fp16, the others in fp32). Jev: median request latency of the hosted API in third-party benchmarks (AbdelStark/jev-benchmarks, nibzard/decision-model-benchmark), which includes the network. Setups differ, so read it as an order-of-magnitude comparison.

Ollaya serves /v1/systemone and /v1/models with TypeSafe's request and response shapes. The official TypeSafe Python SDK 0.7.1 works unchanged against a local server.

Point the TypeSafe SDK at Ollaya

export TYPESAFE_BASE_URL=http://localhost:11435 export TYPESAFE_API_KEY=local # any value works export TYPESAFE_DEFAULT_MODEL=laya

…or call the compatible endpoint directly

curl http://localhost:11435/v1/systemone -d '{ "model": "laya", "state": "Can I get an invoice for last month?", "questions": { "intent": { "type": "choice", "instructions": "What does the customer want?", "criteria": { "invoice": "Needs an invoice or receipt", "refund": "Wants money back", "other": "Anything else" } } } }'

Response

{ "model": "laya:en", "answers": { "intent": { "type": "choice", "choice": "invoice", "confidence": 0.9547, "probabilities": { "invoice": 0.9698, "refund": 0.0172, "other": 0.013 } } }, "usage": { "input_tokens": 43, "output_tokens": 0 } }

TypeSafe compatibility guide

Start with Laya from Convai Innovations: an English model, a 100+ language model, a model fine-tuned for typed decisions, and a router that picks for you.

More open decision models are planned: von, GGUF LLM-based decision models via llama.cpp.

Tickets, emails and user messages are often the most sensitive data you have. With Ollaya they are scored where they already live.

Local

Runs on your machine with ONNX Runtime, on the CPU or an NVIDIA GPU. The server listens on 127.0.0.1 by default.

Open weights

Weights come from their authors’ Hugging Face repositories, pinned to a commit and checked against sha256. Ollaya never re-hosts them, and the runtime is Apache-2.0.

No per-token fees

Run as many decisions as your hardware can handle. No metering and no API bill.

Calibrated

Probabilities you can put thresholds on. Laya’s calibration error (ECE) is 0.081 after temperature fitting, vs 0.246 for Jev.

A desktop app and a command line for macOS, Windows and Linux, and a Docker image for servers. Every model runs on the CPU; an NVIDIA GPU on Linux, in WSL 2 or in Docker takes a request down to milliseconds.

Menu bar app.dmgCommand lineInstall scriptGPUCPU onlyWindows10 and 11, x64Desktop appDesktop app.exe or .msiCommand linePowerShell scriptGPUCPU onlyNVIDIA via WSL 2Linuxx86-64Desktop appDesktop appAppImage, .deb, .rpmCommand lineInstall scriptsystemd serviceGPUNVIDIA, CUDA 13LinuxARM64Desktop appNot availableCommand lineInstall scriptsystemd serviceGPUCPU onlyWSL 2Linux on WindowsDesktop appNot availableCommand lineInstall scriptSame as LinuxGPUNVIDIA, CUDA 13Dockeramd64 and arm64Desktop appNot availableCommand lineImage on GHCRGPUNVIDIA, CUDA 13:cuda image, amd64

Install for your platform NVIDIA GPUs need driver R580 or newer; the installers fetch the CUDA libraries only when they find one. On Apple, AMD and Intel GPUs, models run on the CPU.

Get up and running in minutes.

One binary, one command: ollaya run laya.

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macOS, Windows, Linux and Docker · Apache-2.0 · GitHub