Model compression techniques have been extensively explored to reduce the computational resource demands of serving large language models (LLMs) or other…
Model compression techniques have been extensively explored to reduce the computational resource demands of serving large language models (LLMs) or other large-size neural networks. However, most existing methods either incur significant accuracy degradation compared to uncompressed models or have long training times. Also, their adaptability is often constrained by a limited range of…
