Most popular programs
Trending now
Gain job-ready AI engineering skills for working with transformers, LLM frameworks, and GPT, and learn how to fine-tune models using Hugging Face and PyTorch.
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.
Basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.
Module 0: Welcome
Module 1: Transformers and Fine-Tuning
Module 2: Parameter Efficient Fine-Tuning (PEFT)
Module 3: Course Cheat Sheet, Glossary and Wrap-up
Course Wrap-Up
How do AI engineers, NLP specialists, and machine learning developers fine-tune transformer models like GPT and BERT?
They fine-tune pre-trained models by training them on task-specific datasets to adapt them for applications like sentiment analysis, chatbots, or translation. This involves adjusting model parameters while leveraging the knowledge already embedded in the pre-trained model.
What is fine-tuning in the context of transformer models?
Fine-tuning is the process of adapting pre-trained transformer models like GPT or BERT to perform specific tasks or work within particular domains. By training on task-specific data, these models can deliver more accurate and relevant outputs, which is crucial for applications like customer support, legal document analysis, or creative content generation .
Why is fine-tuning transformer models important for generative AI?
Fine-tuning enables generative AI to produce tailored and precise outputs, aligning with industry-specific needs. For example, fine-tuning GPT can help generate highly contextual responses in healthcare, finance, or education, enhancing the relevance and usability of AI-driven solutions .
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
