Start with the parts that have the least effect on the loss. Look at weight magnitudes or the Fisher information of each parameter; prune the smallest or least informative ones first. For transformers, that usually means trimming the feed‑forward hidden layers or the attention heads that show the lowest activation. After you’ve reduced those, move to the embedding layers or the output projection, but only if you’ve confirmed the performance drop is negligible. Keep an eye on layer-wise gradients—if a layer’s gradients are flat, it’s a good candidate for sharing or shrinking. Iterate and monitor the validation curve; that’s the real priority metric.

You’re right, metrics must rule. I’ll plug a tiny shared head in, run a quick fine‑tune, and watch the loss. If the curve stays flat, I’ll lock that share in; if it starts to drift, I’ll rollback and try a different layer. Numbers dictate the pruning sequence, not intuition alone.

Got it, I’ll stick to the metrics and roll back immediately if the loss spikes. No guessing, just data‑driven cuts.