Parameter-efficient fine-tuning (PEFT) refers to techniques that adapt large pre-trained models to new tasks by training only a small subset of parameters, rather than full model fine-tuning​.This saves computational resources and storage, since most of the model’s original weights remain unchanged​. One popular PEFT method is prefix-tuning, which prepends learned task-specific vectors (a “prefix”) to the model’s input at each layer while keeping the model’s weights frozen​.In practice, prefix-tuning means the model learns a continuous prompt (the prefix) that steers the model’s behavior for the new task, achieving performance comparable to fine-tuning all weights but with only a tiny fraction of parameters being updated​.By only training these additional prefix vectors, a prefix-tuned model stores vastly fewer new parameters (often orders of magnitude fewer) than a fully fine-tuned model, yet can specialize the large language model or neural network to perform the target task​. This approach exemplifies parameter-efficient adaptation, enabling rapid fine-tuning of big models on multiple tasks without the heavy cost of retraining the entire network for each one.