LLM Compression with Jointly Optimizing Architectural and Quantization choices 事件

LLM Compression with Jointly Optimizing Architectural and Quantization choices arXiv:2606.04063v1 Announce Type: cross Abstract: Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements. While some methods address this by developing small or tiny language models from scratch, these approaches demand extensive GPU training. Compressing pre-trained LLMs for edge devices offers a compelling alternative. Beyond pruning and quantization, Ne