Mastering Generative AI: Fine-Tuning Transformers from edX

The demand for technical gen AI skills is exploding. AI engineers who know how to fine-tune transformers for gen AI applications are in hot demand. This Generative AI Engineering Fine-Tuning with Transformers course is designed for AI engineers and other AI specialists who are looking to add highly sought-after skills to their resume.

In this course, you’ll explore the differences between PyTorch and Hugging Face. You’ll use pre-trained transformers for language tasks and fine-tune them for special tasks. Plus, you’ll fine-tune generative AI models using PyTorch and Hugging Face.

You’ll also explore concepts like parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QloRA), model quantization with natural language processing (NLP) and prompting. Plus, through valuable hands-on labs, you’ll build your experience loading models and inference, training models with Hugging Face, pre-training LLMs, fine-tuning models, and PyTorch adaptors.

If you’re looking to gain the job-ready skills employers need for fine-tuning transformers for gen AI, ENROLL TODAY and power up your resume for career success!

Prerequisites: This course requires basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.

What's inside

Learning objectives

Job-ready skills working with transformer-based llms for generative ai engineering employers need in just 2 weeks!
A good understanding of 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.

Job-ready skills working with transformer-based llms for generative ai engineering employers need in just 2 weeks!
A good understanding of 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.

Syllabus

Video: Hugging Face vs. PyTorch

Module 0: Welcome

Video: Course Introduction

Reading: Professional Certificate Overview

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on fine-tuning transformers, which is a highly sought-after skill for AI engineers looking to specialize in generative AI applications

Covers parameter-efficient fine-tuning (PEFT) techniques like LoRA and QLoRA, which are essential for optimizing large language models

Includes hands-on labs for loading models, inference, training with Hugging Face, and fine-tuning, providing practical experience for real-world applications

Requires basic knowledge of Python, PyTorch, and transformer architecture, suggesting it is designed for those with some existing machine learning experience

Uses both PyTorch and Hugging Face, which are current and widely adopted frameworks in the field of generative AI model development and deployment

Presented by IBM, a company recognized for its contributions to AI research and development, lending credibility to the course content

Reviews summary

Fine-tuning transformers for generative ai

Learners say this course provides job-ready skills for Generative AI engineering, particularly focusing on fine-tuning transformers. Students appreciated the excellent coverage of Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA. The hands-on labs using Hugging Face and PyTorch were frequently highlighted as valuable for applying concepts. However, some learners noted that the course has a fast pace and requires solid prerequisite knowledge in Python, PyTorch, and transformer architecture to keep up effectively. Overall, it's seen as a strong course for those meeting the entry requirements.

Effective use of standard tools

"Appreciated the focus on using Hugging Face and PyTorch libraries."

"Working with Hugging Face models felt very relevant to current practices."

"Good examples demonstrating fine-tuning with both frameworks."

Practical labs reinforce learning effectively

"The labs using Hugging Face were great for applying the concepts."

"I found the hands-on exercises to be the most valuable part."

"Being able to load and fine-tune models myself solidified my understanding."

Provides job-ready AI engineering skills

"I feel much more prepared for gen AI roles after taking this course."

"The focus on job-ready skills with transformers is exactly what I needed."

"Helped me understand how to apply these concepts directly to my work."

Excellent coverage of LoRA and QLoRA

"The sections on LoRA and QLoRA were particularly insightful and clear."

"Understanding parameter-efficient fine-tuning was a key takeaway for me."

"The explanation of PEFT methods like LoRA was very thorough."

Requires solid prerequisites; pace can be fast

"Make sure you have a strong PyTorch and transformer background before starting."

"The course moves quickly, so be ready to dedicate focused time."

"I struggled a bit because my Python/PyTorch wasn't as strong as needed."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Generative AI: Fine-Tuning Transformers with these activities:

Review Transformer Architectures

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Solidify your understanding of transformer architectures to better grasp the fine-tuning process.

Browse courses on Transformer Architecture

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Read research papers on transformer architecture.
Watch introductory videos on YouTube.
Summarize the key components of a transformer.

Practice PyTorch Fundamentals

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Strengthen your PyTorch skills to facilitate easier fine-tuning of generative AI models.

Browse courses on PyTorch

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Complete a PyTorch tutorial on the official website.
Implement a simple neural network in PyTorch.
Experiment with different optimizers and loss functions.

Read 'Natural Language Processing with Transformers'

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Gain a deeper understanding of transformers and their applications in NLP.

View Natural Language Processing with Transformers,... on Amazon

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Read the chapters related to fine-tuning and PEFT.
Implement the examples provided in the book.
Compare the book's approach to the course's approach.

Four other activities

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Hugging Face Model Loading and Inference

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Reinforce your ability to load and use pre-trained models from the Hugging Face Hub.

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Load different models from the Hugging Face Hub.
Perform inference with the loaded models.
Experiment with different input prompts and parameters.

Read 'Hugging Face Transformers, 2nd Edition'

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Become more proficient with the Hugging Face Transformers library.

View Putting Knowledge to Work on Amazon

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Read the chapters relevant to the course topics.
Experiment with the code examples in the book.
Use the book as a reference when working on projects.

Fine-Tune a Transformer for Text Summarization

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Apply your knowledge to fine-tune a transformer model for a specific generative AI task.

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Choose a dataset for text summarization.
Fine-tune a pre-trained transformer model on the dataset.
Evaluate the performance of the fine-tuned model.
Compare the results with other summarization techniques.

Write a Blog Post on PEFT Techniques

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Deepen your understanding of parameter-efficient fine-tuning by explaining it to others.

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Research different PEFT techniques like LoRA and QLoRA.
Write a blog post explaining the concepts in simple terms.
Include code examples and visualizations.
Publish the blog post on a platform like Medium or your personal website.

Career center

Learners who complete Mastering Generative AI: Fine-Tuning Transformers will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Engineer

An Artificial Intelligence Engineer develops and implements AI models. Mastering Generative AI: Fine-Tuning Transformers helps AI Engineers by providing hands-on experience with crucial techniques like fine-tuning transformers, using PyTorch and Hugging Face, and exploring parameter-efficient methods such as LoRA and QLoRA. These methodologies are central to modifying pre-trained models, a critical task for adapting them to specific uses. This course provides crucial training in the fine-tuning and adaptation of models, skills which are highly desired in the field.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A Machine Learning Engineer creates and adjusts models for data analysis and prediction. This course, Mastering Generative AI: Fine-Tuning Transformers, is particularly relevant because it delves into the specifics of fine-tuning transformer models, a key component of many machine learning workflows. The course teaches how to work with PyTorch and Hugging Face, crucial tools for any machine learning engineer. Furthermore, it covers parameter-efficient fine-tuning techniques like LoRA and QLoRA, which are essential for optimizing performance.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems to interpret and generate human language. Mastering Generative AI: Fine-Tuning Transformers provides practical skills in fine-tuning transformer models for specific language tasks, which is core to the job of a natural language processing engineer. The course focuses on using pre-trained transformers and adapting them with PyTorch and Hugging Face, including parameter-efficient methods like Low Rank Adaptation. Such a focus on fine-tuning makes this course directly applicable.

See salaries and explore the career path for Natural Language Processing Engineer

Generative AI Specialist

A Generative AI Specialist focuses on creating AI models that generate new data, such as text, images, or music. Mastering Generative AI: Fine-Tuning Transformers prepares one to excel in this role by providing specific experience in fine-tuning transformers, a foundational skill for generative AI. The course covers techniques using PyTorch and Hugging Face and also delves into parameter-efficient fine-tuning (PEFT) with LoRA and QLoRA, which are crucial for optimizing model performance and adaptability for the generative AI specialist.

See salaries and explore the career path for Generative AI Specialist

Deep Learning Engineer

A Deep Learning Engineer creates and implements complex neural network models. This course, Mastering Generative AI: Fine-Tuning Transformers, directly addresses many of the job requirements of a deep learning engineer, especially those working with generative AI models. The course gives hands-on practice fine-tuning transformers, using PyTorch and Hugging Face, and exploring parameter-efficient tuning such as LoRA and QLoRA. These skills provide a robust foundation for anyone aiming to excel as a deep learning engineer.

See salaries and explore the career path for Deep Learning Engineer

AI Research Scientist

An AI Research Scientist explores innovative AI methodologies. Mastering Generative AI: Fine-Tuning Transformers may be useful to Research Scientists because it covers many trending techniques in the field of AI, such as using pre-trained transformers and fine-tuning them for particular tasks with tools like PyTorch and Hugging Face. The course also explores parameter-efficient fine-tuning (PEFT) techniques such as LoRA and QLoRA, which are useful for any kind of experimental AI work. An advanced degree is typically required for this role.

See salaries and explore the career path for AI Research Scientist

Machine Learning Scientist

A Machine Learning Scientist researches and develops new algorithms and models. The course, Mastering Generative AI: Fine-Tuning Transformers, may assist a Machine Learning Scientist by providing a strong foundation in fine-tuning transformer models. The course covers parameter-efficient fine-tuning (PEFT) techniques like LoRA and QLoRA. These kinds of techniques are useful for any kind of experimental model adjustment. An advanced degree is typically required for this role.

See salaries and explore the career path for Machine Learning Scientist

Data Scientist

A Data Scientist analyzes data to extract meaningful insights and build predictive models. While not the primary focus of the role, Data Scientists may find value in “Mastering Generative AI: Fine-Tuning Transformers”. The course explores model fine-tuning, which is useful for adapting models for predictive tasks, and covers relevant tools and concepts like PyTorch, Hugging Face, LoRA, and QLoRA, which may be applicable to a data scientist's machine learning workflows. These things may be helpful in the creation of insights from datasets.

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Computational Linguist

A Computational Linguist develops computational models for processing and understanding human language. Mastering Generative AI: Fine-Tuning Transformers will be useful for a computational linguist because it focuses on techniques essential for transforming pre-trained models for specific uses using PyTorch and Hugging Face. The course also explores parameter-efficient fine-tuning (PEFT) approaches like LoRA and QLoRA, which are helpful for practical applications in computational linguistics. An advanced degree is typically required for this role.

See salaries and explore the career path for Computational Linguist

Research Engineer

A Research Engineer applies scientific and engineering principles to develop new technologies. A Research Engineer who wishes to experiment with new generative AI, this course will help as it provides hands-on skills fine-tuning existing transformers. Mastering Generative AI: Fine-Tuning Transformers covers the practical aspects of working with PyTorch and Hugging Face, along with parameter-efficient tuning practices that are useful for research applications. These include LoRA and QLoRA.

See salaries and explore the career path for Research Engineer

Software Developer

A Software Developer writes and tests code to build software applications. The course, Mastering Generative AI: Fine-Tuning Transformers, may be helpful for Software Developers looking to integrate AI into their applications. The course specifically teaches how to perform fine-tuning using PyTorch and Hugging Face, skills that enable developers to integrate pre-trained models into tools and programs. The course also covers LoRA and QLoRA.

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Data Analyst

A Data Analyst interprets datasets, often using statistical techniques, to inform business decisions. Though different from the core focus of data analysis, a Data Analyst may find that this course, “Mastering Generative AI: Fine-Tuning Transformers,” will be useful. The course offers skills in working with PyTorch and Hugging Face to fine-tune models, which will be useful in more advanced data analysis tasks such as predictive modeling. The course also introduces techniques like LoRA and QLoRA, which may be helpful for modeling.

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Quantitative Analyst

A Quantitative Analyst develops and implements mathematical or statistical models, often in finance. Though not directly related to the core of their work, Mastering Generative AI: Fine-Tuning Transformers may be applicable to a Quantitative Analyst who is interested in experimenting with new machine learning techniques. The course covers fine-tuning models with PyTorch and Hugging Face and introduces techniques like parameter-efficient fine-tuning (PEFT), potentially useful for modeling. An advanced degree is typically required for this role.

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Algorithm Developer

An Algorithm Developer designs and implements algorithms for various applications. While not directly linked, Mastering Generative AI: Fine-Tuning Transformers may be useful for an Algorithm Developer looking to work with generative AI models. The course covers critical techniques of fine-tuning with PyTorch and Hugging Face, as well as parameter-efficient fine-tuning (PEFT) methods like LoRA and QLoRA, which are useful in creating complex algorithms which leverage language models. An advanced degree is typically required for this role.

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Technical Consultant

A Technical Consultant provides expert advice on technology solutions to businesses. While this course, Mastering Generative AI: Fine-Tuning Transformers, may not be central to all types of Technical Consulting, those who provide AI based solutions may find this course incredibly helpful. The course explores how to use transformer models with PyTorch, and Hugging Face, and how to perform parameter-efficient fine-tuning (PEFT) with techniques including LoRA and QLoRA. These skills are vital for those who advise businesses on AI based solutions.

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Mastering Generative AI

Fine-Tuning Transformers

What's inside

Learning objectives

Syllabus

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Reviews summary

Fine-tuning transformers for generative ai

Activities

Career center

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