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microsoft/LoRA

A PyTorch library implementing LoRA (Low-Rank Adaptation) to efficiently fine-tune large language models by significantly reducing trainable parameters and storage requirements.

Core Features

Implements LoRA for parameter-efficient fine-tuning of LLMs.

Drastically reduces the number of trainable parameters.

Minimizes storage footprint for adapted models.

Enables efficient task-switching without adding inference latency.

Achieves comparable or superior performance to full fine-tuning.

Detailed Introduction

LoRA (Low-Rank Adaptation) is a groundbreaking method for efficiently adapting large language models to specific tasks. This project provides a PyTorch implementation, `loralib`, which significantly reduces the number of trainable parameters by learning low-rank decomposition matrices while freezing the original model weights. This approach not only vastly decreases storage requirements for adapted models but also facilitates efficient task-switching during deployment without introducing additional inference latency. LoRA has demonstrated superior or comparable performance against traditional fine-tuning and other parameter-efficient methods like adapter and prefix-tuning, making it a crucial tool for scalable LLM deployment.