Efficient Large Language Model (LLM) Customization (ELLMC)


Course Overview

Enterprises need to execute language-related tasks daily, such as text classification, content generation, sentiment analysis, and customer chat support, and they seek to do so in the most cost-effective way. Large language models can automate these tasks, and efficient LLM customization techniques can increase a model’s capabilities and reduce the size of models required for use in enterprise applications. In this course, you'll go beyond prompt engineering LLMs and learn a variety of techniques to efficiently customize pretrained LLMs for your specific use cases—without engaging in the computationally intensive and expensive process of pretraining your own model or fine-tuning a model's internal weights. Using NVIDIA NeMo™ service, you’ll learn various parameter-efficient fine-tuning methods to customize LLM behavior for your organization.

Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.


  • Professional experience with the Python programming language.
  • Familiarity with fundamental deep learning topics like model architecture, training and inference.
  • Familiarity with a modern Python-based deep learning framework (PyTorch preferred).
  • Familiarity working with out-of-the-box pretrained LLMs.

Course Objectives

By the time you complete this course you will be able to:

  • Apply parameter-efficient fine-tuning techniques with limited data to accomplish tasks specific to your use cases
  • Use LLMs to create synthetic data in the service of fine-tuning smaller LLMs to perform a desired task
  • Drive down model size requirements through a virtuous cycle of combining synthetic data generation and model customization.
  • Build a generative application composed of multiple customized models you generate data for and create throughout the workshop.

Prices & Delivery methods

Online Training

1 day

  • on request
Classroom Training

1 day

  • on request

Currently there are no training dates scheduled for this course.