Efficient Large Language Model (LLM) Customization

About this course

Adapt large language models to your tasks without huge datasets. You will improve results through prompt engineering and prompt learning, apply fine-tuning techniques that work with limited data, run several custom tasks on one model and customise models such as LLaMA-2 and Falcon with the NeMo framework.

What you'll learn

• 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

Curriculum

Who it's for

ML engineers and data scientists deploying LLMs for specialised business tasks.

Before you start

• 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