Efficient Large Language Model (LLM) Customization

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.

• 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

Highly-experienced Python Developers

• 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