A community-garden seed library raises adapter rank so the model can learn more patterns on a few dozen instruction cards. Training loss collapses while validation falls apart. How should rank be treated?
Select an answer to reveal the explanation.
Short Explanation
Raising adapter rank on a few dozen cards can collapse training loss while validation falls apart. Rank is a capacity-versus-overfit knob, not a prestige setting, a Triton flag, or an NCCL world-size.
Full Explanation
LoRA rank controls adapter capacity. On a tiny instruction set, extra rank memorizes the cards and validation collapses. Higher is not more professional; it is a hyperparameter with an overfit trade-off. Serving flags and Professional world-size are unrelated meanings of rank.