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.
Certifications
This course is part of the following Certifications:
Prerequisites
- 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.