Ollaya – Ollama for open-source, Jev-style decision models

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 } } 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. 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. 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. 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. 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. 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. 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.