Generative AI Engineering and Fine-Tuning Transformers
By External Provider
About 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!
What you will 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