Tag Archive for: parameter efficiency

Parameter Efficiency refers to the capacity of a machine learning model to achieve strong performance using a relatively small number of trainable parameters, or to adapt to new tasks by updating only a small subset of an existing model’s weights. Parameter-efficient fine-tuning methods such as LoRA, prefix tuning, and adapter layers enable large pretrained models to be specialized for downstream tasks at significantly reduced computational and memory cost. Research in this area supports scalable deployment of large language and multimodal models across diverse applications.