Jared Palmer Releases Kev Open Source Decision Model Family Based on Qwen3
Jared Palmer announces Kev-0.6B, 4B and 8B, open source Apache 2.0 decision models built on Qwen3 with LoRA and a pointer head. He reports Kev-8B scores 79.6% out of domain versus 85.7% for Jev, and says the models are drop-in compatible with the TypeSafe API.
Original post · 1 min read
This new family is based on Qwen3 using the same LoRA + small pointer head technique as before, but scaled up.
Out of domain, on data Kev never trained on: Kev-8B 79.6%, Jev 85.7%.
• Drop-in TypeSafe System One API; their SDK works with one `base_url` change
• 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: 2-2.5x faster
• Apache 2.0 License. Kev-4B trains in 40 minutes on one H100. Kev-8B in 83 minutes.
Code, weights, evals: github.com/jaredpalmer/kev
Jared Palmer @jaredpalmerKev-0.5B: A tiny open source Jev-like decision model with a TypeSafe-compatible API based on Qwen2.5-0.5B that you can train and run on a MacBook Pro.
Model card and weights are available on GitHub
github.com/jaredpalmer/kev