Ollaya — Run decision models locally  (index.html)                (favicon.svg)  (static/app.css@v=6cf56f58ff.css)   (index.html#main) Skip to content (index.html)        ollaya(Ollaya is new: expect rough edges, and please report what breaks on GitHub) Beta    (https://ollaya.dev/search) Models  (https://ollaya.dev/docs) Docs  (https://github.com/ollaya-dev/ollaya) GitHub    Search models    (Search models)  ⌘K Search    (https://ollaya.dev/download) Download      Search models    (Search models)  Search  (https://ollaya.dev/search) Models  (https://ollaya.dev/docs) Docs  (https://github.com/ollaya-dev/ollaya) GitHub  (https://ollaya.dev/download) Download        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. (https://ollaya.dev/download)   Download (https://ollaya.dev/search) Browse models          ollaya · zsh  $ ollaya run laya --preset triage \  "I was charged twice this month and want a refund."     Answers returned by the model Question Answer Probability   intent refund   1.00    is_urgent no   0.87    frustration 1.59 / 3clearly annoyed    0.36    refund_requested yes   0.88    churn_risk no   0.89      $       Real output: routed to laya:en , answered in 8.9 ms on an RTX 4090.    (index.html#fast) Fast  (index.html#compatible) Drop-in compatible  (index.html#models) Open models  (index.html#private) Your data stays yours  (index.html#platforms) 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. 8–10ms  Laya on Ollaya RTX 4090, five questions, end to end  236–276ms  TypeSafe Jev Hosted API, median request    Every model, one scale · median latency, lower is better  laya:multilingual  8.1 ms   laya:en  9.6 ms   gliclass  14.7 ms   nli  20.4 ms   decider:0.8b  155 ms   decider:2b  190 ms   TypeSafe Jev hosted API    236–276 ms     0 100 200 300 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 ((https://github.com/AbdelStark/jev-benchmarks) AbdelStark/jev-benchmarks , (https://github.com/nibzard/decision-model-benchmark) 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 } }            (https://ollaya.dev/docs/typesafe-compatibility) TypeSafe compatibility guide       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. (https://ollaya.dev/library/laya) laya Open 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   (https://ollaya.dev/library/decider) decider Decoder 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   (https://ollaya.dev/library/nli) nli Zero-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   (https://ollaya.dev/library/gliclass) gliclass Instruction-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    (https://ollaya.dev/search) Browse all models    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   macOS Apple silicon  Desktop app   Menu bar app .dmg    Command line   Install script    GPU   CPU only     Windows 10 and 11, x64  Desktop app   Desktop app .exe or .msi    Command line   PowerShell script    GPU   CPU only NVIDIA via WSL 2     Linux x86-64  Desktop app   Desktop app AppImage, .deb, .rpm    Command line   Install script systemd service    GPU   NVIDIA, CUDA 13     Linux ARM64  Desktop app   Not available    Command line   Install script systemd service    GPU   CPU only     WSL 2 Linux on Windows  Desktop app   Not available    Command line   Install script Same as Linux    GPU   NVIDIA, CUDA 13     Docker amd64 and arm64  Desktop app   Not available    Command line   Image on GHCR    GPU   NVIDIA, CUDA 13 :cuda image, amd64        (https://ollaya.dev/download) 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 . (https://ollaya.dev/download)   Download  macOS, Windows, Linux and Docker · Apache-2.0 · (https://github.com/ollaya-dev/ollaya) GitHub    © 2026 Ollaya (https://ollaya.dev/download) Download  (https://ollaya.dev/docs) Docs  (https://github.com/ollaya-dev/ollaya) GitHub  (https://huggingface.co/ollaya-dev) Hugging Face  (https://ollaya.dev/search) Models