ollaya

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

Website · Models · Docs · Releases · Hugging Face

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 laya --preset triage "I was charged twice for my subscription this month and want a refund."

intent            refund       ████████████████ 1.00
is_urgent         no           ██████████████░░ 0.88
frustration       1.76 / 3     ██████░░░░░░░░░░ 0.36
refund_requested  yes          ██████████████░░ 0.90
churn_risk        no           ██████████░░░░░░ 0.61

Features

• 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 model.safetensors from the author's Hugging Face repository, pinned to a commit and verified by sha256. 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.
  • 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.

Models

Model	What it is
laya	Router: picks laya:en or laya:multilingual by language
laya:en	English decision model (ModernBERT-large, 421M). The fastest: 8–10 ms for five questions on an RTX 4090
laya:multilingual	100+ languages (mmBERT-base, 322M)
laya:typed-decisions	Fine-tuned on the typed-decisions workflows
decider, decider:0.8b	Mapika's Qwen3.5 decoders, 2B and 0.8B. The most accurate: 0.591 on typed-decisions
nli, nli:modernbert-large	Moritz Laurer's zero-shot NLI classifiers (DeBERTa-v3-large, ModernBERT-large)
gliclass	Knowledgator's instruction-following zero-shot classifier (DeBERTa-v3-large)

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.

Install

• 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 R580+), the installer adds the CUDA runtime.
• macOS (Apple silicon): the same command.
• Windows (x64, CPU): irm https://ollaya.dev/install.ps1 | iex in PowerShell. For an NVIDIA GPU, use WSL 2 with the Linux command.
• 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, 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.

Repository

Path	What
crates/ollaya	The binary: CLI, daemon, runner
crates/ollaya-server	HTTP API, scheduler (one runner process per model), model resolution
crates/ollaya-api	API types and client; the contract is docs/api.md
crates/ollaya-registry	Model names, manifests, blob store, resumable pulls
crates/ollaya-decision	Question schema, sequence layouts, calibration, answers
crates/ollaya-runner	Inference engines (ONNX Runtime)
crates/ollaya-lang	Script and language detection for routers
convert/	Build-time Python: ONNX export, parity checks, packaging
site/	The website and the static model registry host

Development

cargo test --workspace
cargo build --release -p ollaya --features cuda   # CUDA build (Linux x86_64)

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.

License

Apache-2.0. Each model keeps its own license: laya (Convai Innovations), decider (Mapika), gliclass (Knowledgator) and nli:modernbert-large are Apache-2.0, and nli:deberta-v3-large (Moritz Laurer) is MIT.