A fire-inspection desk needs token tags for code faults. Full-weight training will not fit tonight's GPU; a parameter-efficient adapter will. What comparison should they run?
Select an answer to reveal the explanation.
Short Explanation
Full-weight training will not fit tonight’s GPU; a PEFT adapter will. Compare them on entity quality, memory, and wall-clock. Serving APIs and how official the method name sounds are not the scoreboard.
Full Explanation
On a named token-classification task, parameter-efficient fine-tuning and a full-weight update are two cost-quality treatments. The associate experiment reports entity quality, memory, and time, especially when the full update will not fit. Serving APIs and method prestige are not the scoreboard. An adapter can be the practical winner if the entity metric stays close.