WINDQuant
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WINDQuant: Weight-Informed Neural Decision-Making for Global Mixed-Precision LLM Quantization
arXiv:2605.26660v2 Announce Type: replace Abstract: Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in the ultra-low-bit regime remains challenging. Existing post-training methods often suffer from severe accuracy degradation, while quantization-aware training requires costly retraining and additional resources.