How long does it take to complete the Specialization?

It takes about 8 hours to complete this course, so you can have the job-ready skills you need to impress an employer within just one week!

Do I need any background knowledge to complete this course successfully?

This course is intermediate level, so to get the most out of your learning, you must have basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.

Which roles will benefit from the skills I will build after completing this course?

This course is part of the Generative AI Engineering with LLMs specialization. When you complete the specialization, you will have the skills and confidence to take on job roles such as AI engineer, NLP engineer, machine learning engineer, deep learning engineer, data scientist, or software developer who want to apply seeking to work with LLMs.

Do I need any specific software or tools to complete the course successfully?

<text variant="body1">Only a modern web browser is required to complete this course and all hands-on labs. You will be provided access to cloud-based environments to complete the labs at no charge.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Generative AI Engineering and Fine-Tuning Transformers

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Generative AI Engineering and Fine-Tuning Transformers

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

23,868 already enrolled

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

114 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.

114 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

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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!

Module details

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 videosTotal 29 minutes

Course Introduction3 minutes
Hugging Face vs. PyTorch6 minutes
Using Pre-Trained Transformers and Fine-Tuning7 minutes
Fine-Tuning with PyTorch7 minutes
Fine-Tuning with Hugging Face5 minutes

4 readingsTotal 25 minutes

Course Overview3 minutes
Specialization Overview7 minutes
Helpful Tips for Course Completion5 minutes
Reading: Summary and Highlights10 minutes

2 assignmentsTotal 27 minutes

Practice Quiz: Transfer Learning in NLP12 minutes
Graded Quiz: Transformers and Fine-Tuning15 minutes

4 app itemsTotal 170 minutes

Lab: Loading Models and Inference with Hugging Face20 minutes
[Optional] Pre-training LLMs with Hugging Face60 minutes
Lab: Pre-Training and Fine-Tuning with PyTorch 60 minutes
Lab: Fine-Tuning Transformers with PyTorch and Hugging Face30 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.