Go to Course: https://www.coursera.org/learn/generative-ai-engineering-and-fine-tuning-transformers
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.
Transformers and Fine-Tuning
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.
Parameter Efficient Fine-Tuning (PEFT)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.
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 A
The labs all too often failed on environment issues - packages, version alignment, etc. This should be seamless in your controlled environment.
The course is good but lacks depth on complex subjects.
The coding part in the labs provided in this course was very helpful and helped me to stabilize my learning.