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Fine-Tuning Transformers and Gen AI Models

Premium
IntermediateCourse

Familiar with Python and PyTorch? Build job-ready skills in Generative AI fine-tuning transformers in 2 weeks! Get practical experience and a credential.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Fine-tuning LLMs, LoRA And QLoRA, Pretraining Transformers, PyTorch, Hugging Face

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 8 hours

Platform

  • SkillsNetwork

Last Update

  • January 14, 2026
About this Course
This Fine-Tuning Transformers and Gen AI Models intermediate-level course teaches you the skills you need for your AI career.  
 
You Will learn: 
 
  • Job-ready skills in 2 weeks, plus you’ll get practical experience employers look for on a resume and an industry-recognized credential 
  • How to perform parameter-efficient fine-tuning (PEFT) using LoRA and QLoRA 
  • How to use pretrained transformers for language tasks and fine-tune them for specific tasks 
  • How to load models and their inferences and train models with Hugging Face. 
 
 
Course Overview 
 
The demand for technical Generative AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Fine-Tuning Transformers and Gen AI Models course builds job-ready skills to advance your AI career.   
 
In this course, you’ll explore transformers, model frameworks, and platforms such as Hugging Face and PyTorch. You’ll begin with a general framework for optimizing LLMs and quickly move on to fine-tuning generative AI models. Further, you’ll learn about PEFT, low-rank adaptation (LoRA), quantized low-rank adaptation (QLoRA), and prompting.   
 
Additionally, you’ll get valuable hands-on experience in online labs that you can talk about in interviews, including loading, pretraining, and fine-tuning models with Hugging Face and PyTorch.   
 
If you’re keen to take your AI career to the next level and boost your resume with in-demand gen AI competencies that catch the eye of an employer, ENROLL today and have job-ready skills you can use straight away within two weeks! 

Course Syllabus

Module 0: Welcome

·         Video: Course Introduction
·         Specialization Overview
·         Reading: Helpful Tips for Course Completion
·         Reading: General Information
·         Reading: Learning Objectives and Syllabus
·         Reading: Grading Scheme

Module 1:Transformers and Fine-Tuning

·         Reading: Module Introduction and Learning Objectives
·         Video: Hugging Face vs. PyTorch
·         Lab: Loading Models and Inference with Hugging
·         Video: Using Pre-Trained Transformers and Fine-Tuning
·         [Optional] Pre-training LLMs with Hugging Face
·         Video: Fine-Tuning with PyTorch
·         Video: Fine-Tuning with Hugging Face
·         Lab: Pre-Training and Fine-Tuning with PyTorch 
·         Lab: Fine-Tuning Transformers with PyTorch and Hugging Face
·         Reading: Summary and Highlights: Transformers and Fine-Tuning
·         Practice Quiz: Transformers and Fine-Tuning
·         Graded Quiz: Transformers and Fine-Tuning

Module 2: Parameter Efficient Fine-Tuning (PEFT)

·         Reading: Module Introduction and Learning Objectives
·         Video: Video: Introduction to PEFT
·         Lab: Adapters with PyTorch
·         Video: Low-Rank Adaptation (LoRA)
·         Video: LoRA with Hugging Face and PyTorch 
·         Lab: LoRA with PyTorch
·         Video: From Quantization to QLoRA
·         [Optional] Lab: QLoRA with Hugging Face
·         Reading: Soft Prompts 
·         Reading: Summary and Highlights: Parameter Efficient Fine-Tuning (PEFT)
·         Practice Quiz: Parameter Efficient Fine-Tuning (PEFT)
·         Graded Quiz:  Parameter Efficient Fine-Tuning (PEFT)
·         Reading: Cheat Sheet: Generative AI Engineering and Fine-tuning Transformers
·         Reading: Course Glossary: Generative AI Engineering and Fine-Tuning Transformers

Course Wrap-Up

·         Reading: Course Conclusion
·         Reading: Congratulations and Next Steps
·         Reading: Teams and Acknolwledgements
·         Reading: Copyrights and Trademarks
·         Course Rating and Feedback
·         Feedback

Recommended Skills Prior to Taking this Course

Basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.

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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Fateme Akbari

Data Scientist @IBM

I'm a data-driven Ph.D. Candidate at McMaster University and a data scientist at IBM, specializing in machine learning (ML) and natural language processing (NLP). My research focuses on the application of ML in healthcare, and I have a strong record of publications that reflect my commitment to advancing this field. I thrive on tackling complex challenges and developing innovative, ML-based solutions that can make a meaningful impact—not only for humans but for all living beings. Outside of my research, I enjoy exploring nature through trekking and biking, and I love catching ball games.

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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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Kang Wang

Data Scientist

I was a Data Scientist in the IBM. I also hold a PhD from the University of Waterloo.

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IBM Skills Network

IBM Skills Network Team

At IBM Skills Network, we know how crucial it is for businesses, professionals, and students to build hands-on, job-ready skills quickly to stay competitive. Our courses are designed by experts who work at the forefront of technological innovation. With years of experience in fields like AI, software development, cybersecurity, data science, business management, and more, our instructors bring real-world insights and practical, hands-on learning to every module. Whether you're upskilling yourself or your team, we will equip you with the practical experience and future focused technical and business knowledge you need to succeed in today’s ever-evolving world.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

Wojciech "Victor" Fulmyk is a Data Scientist and AI Engineer on IBM’s Skills Network team, where he focuses on helping learners build expertise in data science, artificial intelligence, and machine learning. He is also a Kaggle competition expert, currently ranked in the top 3% globally among competition participants. An economist by training, he applies his knowledge of statistics and econometrics to bring a distinctive perspective to AI and ML—one that considers both technical depth and broader socioeconomic implications.

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