Ollaya – Ollama for open-source, Jev-style decision models
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Run decision models locally.
Ask typed questions about any text or JSON and get calibrated answers in milliseconds. Private, open source, on your own hardware.
ollaya run laya --preset triage \"I was charged twice this month and want a refund."| Question | Answer | Probability |
|---|---|---|
| intent | refund | |
| is_urgent | no | |
| frustration | 1.59 / 3 clearly annoyed | |
| refund_requested | yes | |
| churn_risk | no |
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.
8–10ms
Laya on Ollaya
RTX 4090, five questions, end to end
236–276ms
TypeSafe Jev
Hosted API, median request
- laya:multilingual8.1 ms
- laya:en9.6 ms
- gliclass14.7 ms
- nli20.4 ms
- decider:0.8b155 ms
- decider:2b190 ms
- TypeSafe Jevhosted API236–276 ms
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.
Drop-in compatible
Speaks TypeSafe's API.
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.
Request
# 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
}
}Open models
Open weights, ready to pull.
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.
- layaOpen decision models from Convai Innovations. Typed, calibrated answers to choice, score and yes/no questions in a single forward pass, in English and 100+ languages.322m · 421m
- deciderDecoder decision models by Mapika on Qwen3.5: the answer is read from option-letter logits in one forward pass. The most accurate open decision model Ollaya ships.0.75b · 1.9b
- nliZero-shot classifiers by Moritz Laurer: every option becomes a hypothesis scored for entailment. The most accurate encoder model on typed decisions in our tests.396m · 435m
- gliclassInstruction-following zero-shot classifier by Knowledgator: all options of a question are scored in one pass, so cost barely grows with the number of options.439m
More open decision models are planned: von, GGUF LLM-based decision models via llama.cpp.
Your data stays yours
Private by default.
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.
Platforms
Runs where you work.
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.
| Platform | Desktop app | Command line | GPU |
|---|---|---|---|
| macOSApple silicon | Desktop appMenu bar app.dmg | Command lineInstall script | GPUCPU only |
| Windows10 and 11, x64 | Desktop appDesktop app.exe or .msi | Command linePowerShell script | GPUCPU onlyNVIDIA via WSL 2 |
| Linuxx86-64 | Desktop appDesktop appAppImage, .deb, .rpm | Command lineInstall scriptsystemd service | GPUNVIDIA, CUDA 13 |
| LinuxARM64 | Desktop appNot available | Command lineInstall scriptsystemd service | GPUCPU only |
| WSL 2Linux on Windows | Desktop appNot available | Command lineInstall scriptSame as Linux | GPUNVIDIA, CUDA 13 |
| Dockeramd64 and arm64 | Desktop appNot available | Command lineImage on GHCR | GPUNVIDIA, CUDA 13:cuda image, amd64 |
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.
macOS, Windows, Linux and Docker · Apache-2.0 · GitHub
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