Ementorhub

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

Large Language Modeling

PyTorch (Machine Learning Library)

Deep Learning

Prompt Engineering

Generative AI

Performance Tuning

Natural Language Processing

There are 2 modules in this course

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!

Parameter Efficient Fine-Tuning (PEFT)