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WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Pruning is a promising approach for improving the efficiency of large language models (LLMs). Static structured pruning is hardware-friendly, but its input-agnostic computation allocation often degrades model quality at high sparsity. Dynamic pruning adapts computation to individual tokens, but coar
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