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.
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 +2 more
8,548 already enrolled
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4.6
(62 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
- Category: Large Language Modeling
- Category: Prompt Engineering
- Category: Generative AI
- Category: Performance Tuning
- Category: Natural Language Processing
- Category: Deep Learning
- Category: PyTorch (Machine Learning Library)
Details to know
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Assessments
4 assignments
Taught in English
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There are 2 modules in this course
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
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.
What's included
4 videos5 readings2 assignments2 app items4 plugins
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Instructors
Instructor ratings
4.7 (6 ratings)Joseph Santarcangelo
IBM
35 Courses1,982,745 learners
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IBM
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4.6
62 reviews
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Showing 3 of 62
RK
4
Reviewed on Jan 17, 2025
The labs all too often failed on environment issues - packages, version alignment, etc. This should be seamless in your controlled environment.
SS
5
Reviewed on Nov 17, 2024
The coding part in the labs provided in this course was very helpful and helped me to stabilize my learning.
AE
4
Reviewed on Jan 2, 2025
The course is good but lacks depth on complex subjects.
Frequently asked questions
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!
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.
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.