Efficient Large Language Model

Efficient Large Language Models (LLMs) aim to reduce the substantial computational cost and memory demands of current LLMs while maintaining high performance. Research focuses on optimizing model architectures (e.g., exploring alternatives to Transformers, employing linear attention mechanisms, and utilizing state space models), developing efficient training and inference techniques (like knowledge distillation, pruning, quantization, and parameter-efficient fine-tuning), and leveraging hardware optimizations (including specialized hardware and heterogeneous GPU allocation). These advancements are crucial for making LLMs more accessible and deployable in resource-constrained environments, broadening their applicability across various scientific domains and practical applications.