Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization 事件

Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization arXiv:2603.16105v3 Announce Type: replace Abstract: Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches have been proposed, less emphasis has been placed on selecting the most suitable set of data (the so-called \emph{calibration data}) for finding the compressed model configuration. The