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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

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

本文标签： 示例工作原理模型代码LLMs

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