NLP and LLMs - Fine-Tuning

About

Fine-tuning is a method of modifying generalized LLMs to work to have better performance in specific domains.

Learning Objectives

You will be able to:

Describe fine-tuning use cases.
Explain the goals and tradeoffs of model fine-tuning and methods (LoRA, QLoRA, etc.).
Apply Low-Rank Adaption (LoRA) of LLMs.

Content

Topic	About
Slides	- Describe fine-tuning use cases - Explain the goals and tradeoffs of model fine-tuning and methods (LoRA, QLoRA, etc.)
Low Rank Adaptation (LoRA)	- Apply Low-Rank Adaption of LLMs

Prerequisites

Write and run Python code in a Jupyter notebook.
Create basic Python functions.
Import and use pandas and numpy.
Understand how LLMs work.
Able to use Hugging Face.

References

📖 Reference Materials

Important note

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