Run open decision models locally, the way Ollama runs LLMs.

Website · Models · Results · Docs · Releases · Hugging Face

Created and maintained by Mert Cobanov (@mertcobanov).

A decision model reads a state (a message, an email, a ticket, any JSON) plus typed questions (choice, score, noul) and returns calibrated probabilities in a single forward pass, in milliseconds. It never generates text. Ollaya pulls these models by name, serves them from a local daemon, and speaks TypeSafe's /v1/systemone wire format, so existing Jev clients work by changing one environment variable.

curl -fsSL https://ollaya.dev/install.sh | sh
ollaya run winnow:e4b --preset triage "Third time this year you've double-charged me. Refund it today or I'm cancelling and moving to a competitor."

intent            refund                                ███████████████░ 0.91
is_urgent         yes                                   ███████████████░ 0.92
frustration       2.89 / 3  very angry or using stron…  ██████████████░░ 0.86
refund_requested  yes                                   ████████████████ 0.99
churn_risk        yes                                   ████████████████ 0.99

winnow:e4b is the recommended model: 0.722 accuracy on typed decisions (TypeSafe's Jev: 0.738) and 89 ms for these five questions on an RTX 4090. It is a 4B-class language model, so without an NVIDIA GPU start with laya, which answers in a fraction of a second on a CPU. All models and their numbers: ollaya.dev/search.

• One binary. ollaya serve runs the daemon; ollaya run, pull, list, ps, show, rm, cp, stop and create work the way they do in Ollama. If the daemon isn't running, the CLI starts it.
• TypeSafe-compatible. POST /v1/systemone, /v1/decisions and GET /v1/models are wire-identical to TypeSafe. The official SDK works unchanged when you set TYPESAFE_BASE_URL=http://localhost:11435.
• Native API. /api/decide adds routing information and timings. /api/pull streams NDJSON progress, and there are /api/tags, /api/show, /api/ps and more. See docs/api.md.
• Weights come from their authors. Ollaya publishes only small ONNX graphs, about 3 MB each. These graphs read the original weight files (usually model.safetensors) from the author's Hugging Face repository, pinned to a commit and verified by sha256. Models whose authors publish GGUF files (winnow, jevk5, jeb) run that file itself on llama.cpp. Ollaya never re-hosts weights.
• For agents. ollaya mcp serves the models to Claude Code, Claude Desktop, Cursor and other MCP clients (claude mcp add ollaya -- ollaya mcp), and the ollaya-decisions skill teaches agents when and how to use them (npx skills add ollaya-dev/ollaya --skill ollaya-decisions).
• Routers. laya detects the script and language of each request, then answers with laya:en or laya:multilingual.
• Modelfiles. You can bake a question set into your own model:

  FROM laya
  QUESTIONS ./triage.json
  PARAMETER precision fp32
  

  Then run ollaya create triage -f Modelfile and ollaya run triage "…".
• Fast and exact.
  • Hardware: ONNX Runtime on CPU, and CUDA on NVIDIA GPUs. GGUF models run on llama.cpp: CPU, CUDA, and Metal on Apple silicon.
  • Precision: fp16 on GPU and fp32 on CPU, chosen when the model loads.
  • Accuracy: fp32 exports give the same decision as the PyTorch reference on 100% of 2,383 questions per checkpoint.

Browse them at ollaya.dev/search. Laya tags ending in -fp32 or -fp16 pin the precision. The derived files of every model are also published at huggingface.co/ollaya-dev.

Ollama 0.35 also serves decision models: Nimble and Tev1, through the same TypeSafe wire format. The FAQ compares the two projects.

We measure every model on our own GPUs and CPUs and publish all of it, with the raw data, at ollaya.dev/results: accuracy and calibration on public benchmarks, speed on every machine, and parity with the authors' own code on each device.

• Accuracy. On Bespoke Labs' public benchmark (3,880 human-labeled questions from 13 datasets, scored with Bespoke's own code, one RTX 5090), winnow:12b scores 0.773 at 60 ms per question. Ollama's best, Nimble, scores 0.749 at 210 ms.
• Calibration. On the same Nimble weights, the calibration error is 0.022 on Ollaya and 0.122 on Ollama: Ollaya applies each model's fitted temperature.
• Speed. Every model on an RTX 5090, an RTX 4090 and two CPUs (Threadripper 3960X, i9-13900K), five questions per request through the HTTP API. The encoders (laya, nli, gliclass, von, qwen3guard) take 7 to 35 ms on a GPU and 0.3 to 2.4 s on a CPU; the decoders 0.1 to 0.9 s on a GPU and 1.3 to 25 s on a CPU; nimble:9b 1.8 to 2.3 s on a GPU.
• Parity. Before a model ships, its runtime is checked question by question against the authors' code (or llama.cpp's own server, for GGUF models) on each device it runs on.

• Linux (x86_64 or arm64, glibc ≥ 2.38, e.g. Ubuntu 24.04+): curl -fsSL https://ollaya.dev/install.sh | sh. When an NVIDIA GPU is present (driver R525+), the installer adds the CUDA runtime: CUDA 13 for R580+, CUDA 12 for older drivers.
• macOS (Apple silicon): the same command.
• Windows (x64): irm https://ollaya.dev/install.ps1 | iex in PowerShell. When an NVIDIA GPU is present (driver R527+), the installer adds the CUDA runtime, as on Linux.
• Desktop app for macOS, Windows and Linux: start and stop the server, download models and run them in one window. On macOS it lives in the menu bar. Get it from ollaya.dev/download.
• Docker: docker run -d --gpus=all -p 11435:11435 ghcr.io/ollaya-dev/ollaya:cuda (:cuda12 for host drivers older than R580), or ghcr.io/ollaya-dev/ollaya for CPU only.

Configuration is through environment variables: OLLAYA_HOST, OLLAYA_MODELS, OLLAYA_KEEP_ALIVE, OLLAYA_DEVICE, OLLAYA_API_KEY and others, listed in docs/api.md §15.

cargo test --workspace
cargo build --release -p ollaya --features cuda   # CUDA build (x86-64 Linux and Windows)

convert/ rebuilds models. It exports them, checks parity against the PyTorch reference, generates golden fixtures, and packages the result into registry/. See the module docstrings. cd convert && uv sync installs it: with CUDA 13 torch on Linux and Windows, and with the CPU and MPS build from PyPI on Apple silicon Macs, where exports and parity run on the CPU.

Apache-2.0. Each model keeps its own license: laya (Convai Innovations), decider (Mapika), kev (Jared Palmer, on Qwen3.5 by the Qwen team), decision (the vLLM Semantic Router contributors, on Qwen3.5), qwen3guard (Qwen team), gliclass (Knowledgator), von (Victor Hugo Panisa), winnow (EldanRing, on Gemma 4 by Google DeepMind), jevk5 (alibiserikbay, on Qwen3.5), nimble (Bespoke Labs, on Qwen3.5), jeeves (PostHog, on Qwen3.5), clef (Cloudflare, on Qwen3.5), jeb (Jason Brashear, on Qwen3.5 and Qwen3.8), cygnet (Gemma 4 by Google DeepMind; the Cygnet recipe is MIT) and nli:modernbert-large are Apache-2.0, and nli:deberta-v3-large (Moritz Laurer) is MIT. llama.cpp, which Ollaya ships for GGUF models, is MIT.

Ollaya is an independent project. It is not affiliated with or endorsed by Ollama or TypeSafe.