Kev offers self-hosted Qwen3.5 decision models with training code and evaluation data
Kev is a family of small Qwen3.5-based decision models that developers can train and run themselves, according to its GitHub documentation. The project supports yes/no questions, multiple-choice decisions and ratings in one request, returning probabilities rather than just labels. It offers a local route to structured text decisions, with documented hardware and accuracy trade-offs.
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The README lists 0.8B, 4B and 9B models, pretrained weights, training code and evaluation data, and reports an update on September 21, 2026. Questions share source text but are isolated from one another; answer options within a question can still influence results through their order. A playground lets users test that sensitivity. The project says its 4B and 9B models fit a 32 GB Mac, but recommends the older Qwen3 versions when low latency matters on Apple Silicon. Its Jev comparison is explicitly not controlled because Jev’s training datasets are unknown. Confidence fields are not measured accuracy rates, and the local server has no authentication unless users add it.
Kev makes structured AI decisions available locally, but its documentation draws useful boundaries: probabilities are not accuracy guarantees, option order can matter, and fitting in memory does not mean running fast.
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Read the original reporting: Jared Palmer / Kev GitHub documentation ↗
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