kev

What it is

Kev is a family of small decision models built on Qwen3 and based on the architecture described in Jev's Architecture Unmasked. You can use the pretrained weights or train your own. The API matches TypeSafe's System One, so you can point their Python SDK at your local server.

What the README says it does

• Yes/no (noul), multiple-choice (choice) and rating (score) questions in one request. The questions share the text but can't read each other.
• Calibrated probabilities by default: each checkpoint ships with a fitted temperature.
• Drop-in for Jev: the TypeSafe Python SDK works against a Kev server unchanged.
• Four sizes, versioned together as Kev 1.0: from a 0.8B that runs on a laptop to a 27B for a single data-centre GPU.
• Documents of up to 65,536 tokens, on CUDA and on Apple Silicon through MLX. Each model card says how long a document can get before accuracy drops.

From the project’s README.

Getting started

From the README:

git clone https://github.com/jaredpalmer/kev.git && cd kev
uv sync --extra serve
uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8009

Where the decision sits

kev calls the hosted decision API rather than running its own weights. That keeps the project small and means its behaviour moves whenever the vendor ships a new model version. It also means an API key and a network round trip sit in the path of every decision.

Models it references: Jev.

How active it is

Created 2026-09-17, last pushed 2026-10-27, 9,760 stars and 668 forks when we read it on 2026-11-02. Everything in this category is weeks old at most, so a stale push date means something different here than it would on a mature project. Check the repository before you depend on it.