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Efficient fine-tuning of neural nets using LoRA and PyTorch

IntermediateGuided Project

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.

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 56

Skills You Will Learn

  • Generative AI, Natural Language Processing, Deep Learning, Python, Artificial Intelligence, PyTorch

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • October 21, 2025
About this Guided Project

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.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Ashutosh Sagar

Data Scientist

I am currently a Data Scientist at IBM with a Master’s degree in Computer Science from Dalhousie University. I specialize in natural language processing, particularly in semantic similarity search, and have a strong background in working with advanced AI models and technologies.

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Wojciech "Victor" Fulmyk

Data Scientist at IBM

I am a data scientist and economist with a strong background in econometrics, time series analysis, causal inference, and statistics. I stand out for my ability to combine technical expertise with clear communication, turning complex data findings into practical insights for stakeholders at every level. Follow my projects to learn about data science principles, machine learning algorithms, and artificial intelligence agents.

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