// HACKER NEWS — CYBERSECURITY
Kev: Tiny Jev-like family of decision models built on top of Qwen3.5
Small Jev-like decision models you can train and run yourself.
Kev is a family of small decision models built on Qwen3.5 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.
This starts Kev-4B locally. The first run downloads the adapter and base model. --run also accepts a local checkpoint directory or a Hub revision, such as jaredpalmer/kev-4b@qwen3 for the previous generation.
Example response from Kev-4B, running in bf16 on an Apple M5:
The ticket mentions a return, a late delivery, and a billing problem, and the department probabilities say so. That is the point of getting probabilities back instead of a single label.
The TypeSafe SDK is included in uv sync --extra serve:
With the server still running, open another terminal. You'll need Node 20.9+:
Open localhost:3001, load a preset, and edit the text and questions. Press ⌘↵ to run it. "Packed vs separate" compares asking all questions at once with asking them one at a time. "Permute" runs a Choice question with six option orders. There are also presets for testing question isolation and fake delimiter tokens.
There's a chess demo, too. The board is the input, legal moves are Choice options, and a Score question rates the position. You can play against Kev or let it play itself. Games are saved in localStorage.
Start with Kev-4B. Use Kev-9B when accuracy and calibration matter more than memory. Use Kev-0.8B if you need the smallest model. All three are built on Qwen3.5 bases with the same training data and settings.