Kev open-source decision models scale to 0.6B/4B/8B, trainable in 40 min on one H100
TheMoonMidas · x · 2026-09-21
Jared Palmer released Kev, a family of open-source, Jev-like small decision models in 0.6B, 4B, and 8B sizes, built on Qwen3 with a LoRA adapter plus a small pointer head.
How it works: one forward pass reads a document and answers many typed questions in parallel — document and questions are packed into one sequence with a block-causal mask so questions see the document but not each other; a pointer head softmaxes calibrated probabilities with no autoregressive decoding.
Key numbers and features:
- Out of domain, Kev-8B scores 79.6% vs Jev's 85.7%
- Drop-in TypeSafe System One API — works by changing one baseurl
- Kev-4B serves on a 32 GB Mac in bf16 (300 ms for five questions, 40 ms on an H100); repeated documents hit a KV cache for 2-2.5x speedup
- Apache 2.0; Kev-4B trains in 40 minutes on one H100, Kev-8B in 83 minutes
Code, weights, and evals are on GitHub.
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