At a Glance

Name: Efficient fine-tuning of neural nets using LoRA and PyTorch
Price: Free CAD
Rating: 4.6 (5 reviews)
Author: joseph_santarcangelo, ashutosh_sagar, wojciech_fulmyk

Fine-tune neural networks using Low-Rank Adaptation (LoRA) in Python and PyTorch. Start by pretraining a model on the AG News data set, which allows it to develop extensive news categorization skills. Then apply LoRA to further refine this model on the IMDB data set, with a focus on sentiment analysis. Discover how LoRA delivers outstanding results while training a smaller number of parameters compared to traditional fine-tuning approaches.

A Look at the Project Ahead

Fine-tuning is a process that demands significant computational resources and time. It usually entails unfreezing certain layers of a pretrained model, which necessitates the adjustment of weights for all the unfrozen layers. However, there's an alternative in the form of LoRA (Low-Rank Adaptation). This method allows for the adjustment of a much smaller number of weights and enhancing efficiency compared to the traditional fine-tuning process. In this hands-on guided project, you acquire the skills to use LoRA with Python and PyTorch. This involves fine-tuning a model that has been trained on the AG News data set, and applying it to perform sentiment analysis on the IMDB movie reviews data set.

Learning objectives

Upon completion of this project, you have the ability to:

Construct and train a neural network from the ground up
Fine-tune a neural network in the conventional manner by unfreezing specific layers
Use LoRA to fine-tune a neural network
Comprehend the functions of LoRA and the reasons behind its effectiveness
Save and load models that employ LoRA efficiently

Overview

In this project, the model is first pretrained on the AG News data set, learning broad news categorization. Then, the pretrained model is fine-tuned on the IMDB data set, specializing in sentiment analysis.

Steps:

1. Pretraining on AG News

Categories: World, Sports, Business, Science.
Purpose: Establish a robust base of language understanding.

2. Applying LoRA

LoRA technique is used to adapt the model efficiently by modifying the attention layers.
This step reduces the number of parameters to fine-tune, which enhances efficiency.

3. Fine-tuning on IMDB

Focus: Positive and negative movie reviews.
Purpose: Adapt the model to understand and analyze sentiment in movie reviews.

Benefits:

• Efficiency: LoRA reduces the computational resources that are needed for fine-tuning.

• Transfer learning: Uses the broad understanding from AG News to specialize in a different domain (IMDB).

• Performance: Achieves high accuracy in sentiment analysis by building on a well-trained base model.

By following this method, the model effectively transitions from general news categorization to specific sentiment analysis tasks, which showcases the power of LoRA in optimizing machine learning workflows.

What You'll Need

For this project, you need an intermediate level of proficiency in Python, PyTorch, and deep learning. There’s no prerequisite for experience with or knowledge of LoRA. Additionally, the only equipment that you need is a computer equipped with a modern browser, such as the latest versions of Chrome, Edge, Firefox, or Safari.

Offered By: IBMSkillsNetwork

Efficient fine-tuning of neural nets using LoRA and PyTorch

At a Glance

A Look at the Project Ahead

Learning objectives

Overview

What You'll Need