SPRINT prunes Transformer sublayers using latency-aware importance scores and post-tuning sensitivity, achieving better accuracy-speedup trade-offs on Llama models than prior pruning methods.
This demonstrates that five candidates are sufficient to find the sublayer with the least importance score
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Accurate Sublayer Pruning for Large Language Models by Exploiting Latency and Tunability Information
SPRINT prunes Transformer sublayers using latency-aware importance scores and post-tuning sensitivity, achieving better accuracy-speedup trade-offs on Llama models than prior pruning methods.