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Course Overview
In this course, you'll go beyond using out-of-the-box pretrained 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 the open-source NVIDIA NeMo™ framework, you’ll learn prompt engineering and various parameter-efficient fine-tuning methods to customize LLM behavior for your organization.
Who Should Attend
Highly-experienced Python Developers
Course Objectives
- Use prompt engineering to improve the performance of pretrained LLMs
- Apply various fine-tuning techniques with limited data to accomplish tasks specific to your use cases
- Use a single pretrained model to perform multiple custom tasks
- Leverage the NeMo framework to customize models like GPT, LLaMA-2, and Falcon with ease
Course Outline
- Introduction
- Engineering Effective Prompts
- Customized Prompt Learning
- Parameter-Efficient Fine-Tuning (PEFT) and Supervised Fine-Tuning (SFT)
- Assessment and Q&A