arXiv (OAI Expanded)
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Forward-Free LLM Depth Pruning via Weight Redundancy
Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states through forward passes on calibration data, while existing forward-free methods score each Transformer block separately without measuring similarity
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