Low-Rank Adaptation for Fine-tuning
Introduction
Fine-tuning is a common technique used in transfer learning, where a pre-trained model is further trained on a specific task. However, fine-tuning can be computationally expensive and memory-intensive, especially for large models. Low-rank adaptation is a technique that addresses these issues by reducing the model’s rank while preserving its performance. In this article, we will delve into the principles of low-rank adaptation for fine-tuning and provide a code implementation example.
微调是迁移学习中常用的技术，其中预训练的模型针对特定任务进行进一步训练。然而，微调可能
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