Generative AI Engineering and Fine-Tuning Transformers

Generative AI Engineering and Fine-Tuning Transformers

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

Access provided by Sadhana Shikshan Mandal - Saraswati College

20,903 already enrolled

2 modules

Gain insight into a topic and learn the fundamentals.

106 reviews

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

106 reviews

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Sought-after, job-ready skills businesses need for working with transformer-based LLMs in generative AI engineering
How to perform parameter-efficient fine-tuning (PEFT) using methods like LoRA and QLoRA to optimize model training
How to use pretrained transformer models for language tasks and fine-tune them for specific downstream applications
How to load models, run inference, and train models using the Hugging Face and PyTorch frameworks

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

4 assignments

Taught in English

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There are 2 modules in this course

The demand for technical generative AI (GenAI) skills is increasing, and businesses are actively seeking AI engineers who can work with large language models (LLMs). This IBM course is designed to build job-ready skills that can accelerate your AI career.

In this course, you’ll explore transformers and key model frameworks and platforms, including Hugging Face and PyTorch. You’ll begin with a foundational framework for optimizing LLMs and quickly advance to fine-tuning generative AI models. You’ll also learn advanced techniques such as parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized LoRA (QLoRA), and prompting. The hands-on labs will give you valuable, practical experience including loading, pretraining, and fine-tuning models using industry-standard tools. These skills are directly applicable in real-world AI roles and are great for showcasing in interviews. If you’re ready to take your AI career to the next level and strengthen your resume with in-demand Gen AI competencies, enroll today and start applying your new skills in just one week!

In this module, you will delve into the practical aspects of working with large language models (LLMs) using industry-standard tools like Hugging Face and PyTorch. You’ll explore the distinctions between these frameworks, learn how to load and perform inference with pretrained models, and understand the processes of pretraining and fine-tuning LLMs. Through hands-on labs, you’ll gain experience in implementing these techniques, enhancing your ability to develop and optimize generative AI models for various applications. By the end of this module, you’ll be equipped with the skills to effectively utilize and fine-tune LLMs, aligning them with specific tasks and performance requirements.

What's included

5 videos4 readings2 assignments4 app items

5 videos Total 29 minutes

Course Introduction 3 minutes
Hugging Face vs. PyTorch 6 minutes
Using Pre-Trained Transformers and Fine-Tuning 7 minutes
Fine-tuning with PyTorch 7 minutes
Fine-Tuning with Hugging Face 5 minutes

4 readings Total 25 minutes

Course Overview 3 minutes
Specialization Overview 7 minutes
Helpful Tips for Course Completion 5 minutes
Reading: Summary and Highlights 10 minutes

2 assignments Total 27 minutes

Practice Quiz: Transfer Learning in NLP 12 minutes
Graded Quiz: Transformers and Fine-Tuning 15 minutes

4 app items Total 170 minutes

Lab: Loading Models and Inference with Hugging Face 20 minutes
[Optional] Pre-training LLMs with Hugging Face 60 minutes
Lab: Pre-Training and Fine-Tuning with PyTorch 60 minutes
Lab: Fine-Tuning Transformers with PyTorch and Hugging Face 30 minutes

In this module, you will explore cutting-edge methods for fine-tuning large language models using parameter-efficient fine-tuning (PEFT) techniques. You’ll gain an understanding of adapters, low-rank adaptation (LoRA), and quantization, along with practical applications of PyTorch and Hugging Face libraries. The hands-on labs and readings will deepen your knowledge of soft prompts, quantized LoRA (QLoRA), and key terminology. You will also have access to a concise cheat sheet and a glossary that reinforce essential techniques, terms, and tools introduced throughout the course.