Finetuning

Reach for this when you're choosing between SFT, LoRA, DPO, GRPO, or any other post-training method, or when debugging why your training run isn't moving the needle. It starts with a decision tree keyed to reward shape: verifiable benchmarks get RL, preference pairs get DPO, demonstrations get SFT. The diagnostics are practical: stuck at zero on a verifiable task means you picked the wrong technique class, not that you need to tune hyperparameters. It enforces smoke runs on ten examples before you burn GPU budget, builds mid-training eval and early stopping directly into scripts, and caps retries at one for training-scale experiments. The literature brief pattern via evo:ideator is smart, most teams skip that step and waste time rediscovering what already works for their model family.

Install to Claude Code

npx -y skills add evo-hq/evo --skill finetuning --agent claude-code