● Covered by 1 source · 1 reportLow impact1 neutral
Ollaya provides local, fast, and private open-source decision models compatible with TypeSafe API
Key points
- Ollaya runs decision models locally on CPU or NVIDIA GPU.
- Models provide answers in milliseconds via a single forward pass.
- It is drop-in compatible with TypeSafe's API.
- Supports open models like Laya, Decider, NLI, and GLICLASS.
Local and Fast Decision Models
Ollaya enables users to run Jev-style decision models directly on their local machines, utilizing either a CPU or an NVIDIA GPU. This local execution ensures that decisions are made quickly, with response times for a five-question request to Laya on an RTX 4090 averaging around 10 ms end-to-end through its HTTP API. The models answer in a single forward pass, eliminating token-by-token generation.
TypeSafe API Compatibility
Ollaya is designed for drop-in compatibility with TypeSafe's API, serving /v1/systemone and /v1/models endpoints with TypeSafe's request and response shapes. This compatibility means that the official TypeSafe Python SDK 0.7.1 can operate unchanged against a local Ollaya server, simplifying integration for developers already using TypeSafe.
Open Models and Privacy
The platform supports various open decision models, including Laya from Convai Innovations, Decider by Mapika, NLI by Moritz Laurer, and GLICLASS by Knowledgator. These models offer capabilities for choice, score, and yes/no questions in multiple languages. A key feature of Ollaya is its privacy-by-default approach, as all data processing occurs locally, ensuring sensitive information like tickets, emails, and user messages remain on the user's machine.
✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →
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Primary sources
GitHub ollaya-dev/ollaya GitHub AbdelStark/jev-benchmarks GitHub nibzard/decision-model-benchmarkReporting from
Ollaya offers an open-source solution for running Jev-style decision models locally, providing fast inference times and privacy by processing data on the user's machine. This allows developers to integrate decision-making AI into applications without relying on external APIs, ensuring data remains private.