Generative AI Engineering and Fine-Tuning Transformers

Generative AI Engineering and Fine-Tuning Transformers

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

What you'll learn

Sought-after job-ready skills businesses need for working with transformer-based LLMs for generative AI engineering... in just 1 week.
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.

Details to know

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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 gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Generative AI Engineering and Fine-Tuning Transformers course builds job-ready skills that will power your AI career forward.

During 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. Plus, you’ll learn about parameter-efficient fine-tuning (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 a week!

In this module, you will be introduced to Fine Tuning. You’ll get an overview of generative models and compare Hugging Face and PyTorch frameworks. You’ll also gain insights into model quantization and learn to use pre-trained transformers and then fine-tune them using Hugging Face and PyTorch.

What's included

5 videos4 readings2 assignments4 app items

5 videosTotal 29 minutes

Course Introduction3 minutesPreview module
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

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

4 app itemsTotal 170 minutes

Lab: Loading Models and Inference20 minutes
Pre-training LLMs with Hugging Face60 minutes
Lab: Pre-training and Fine Tuning with PyTorch 60 minutes
Lab: Fine-Tuning Transformers with Hugging Face30 minutes

In this module, you will gain knowledge about parameter efficient fine-tuning (PEFT) and also learn about adapters such as LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation). In hands-on labs you will train a base model and pre-train LLMs with Hugging Face.