Low-Rank-Adaptation Study and Practice

This repository is dedicated to studying and practicing Low-Rank Adaptation (LoRA) techniques. It serves as a comprehensive resource for foundational knowledge as well as advanced applications in various domains such as image classification, detection, and large language models (LLMs).

Table of Contents

Introduction
Getting Started
Basic Concepts
Projects and Implementations

1. Vision Transformer (ViT) with LoRA for Image Classification
2. Object Detection with LoRA
3. LoRA in Large Language Models (LLMs)
   Resources
   Contributing
   License

Introduction

Low-Rank Adaptation (LoRA) is a powerful technique used to enhance the performance of various machine learning models by reducing the rank of weight matrices in neural networks. This repository aims to gather all the knowledge, experiments, and implementations related to LoRA, from basic concepts to advanced applications.

Getting Started

To get started with this repository, you can clone it using the following command:

git clone https://github.com/your-username/Low-Rank-Adaptation-Study.git
cd Low-Rank-Adaptation-Study

Ensure you have the necessary dependencies installed. You can set up the environment using:

pip install -r requirements.txt

Basic Concepts

In this section, we cover the foundational concepts of Low-Rank Adaptation, including theoretical background, mathematical formulations, and simple examples to illustrate the basic principles.

Projects and Implementations

1. ViT with LoRA for Image Classification This project demonstrates the application of LoRA to Vision Transformers (ViT) for image classification tasks. We provide detailed explanations, code, and results for training ViT models with LoRA.

2. Object Detection with LoRA Here, we explore how LoRA can be utilized in object detection frameworks. The implementation includes model training, evaluation, and comparison with standard object detection models.

3. LoRA in Large Language Models (LLMs) In this section, we investigate the integration of LoRA into large language models. We provide scripts, notebooks, and analyses on how LoRA affects the performance and efficiency of LLMs.

Resources

Papers and Articles Tutorials and Guides Datasets Tools and Libraries

Contributing

We welcome contributions from the community. If you have any suggestions, bug reports, or want to add new content, please open an issue or submit a pull request.

License

This repository is licensed under the MIT License. See the LICENSE file for more details.