Hyperparameter Tuning
In this chapter, you’ll learn about the hyperparameters in LLMs and strategies for optimizing them efficiently. We’ll explore both manual and automated tuning approaches, including grid search, random search, and more advanced methods, such as Bayesian optimization and population-based training. You’ll also gain insights into handling multi-objective optimization scenarios common in LLM development.
By the end, you’ll be equipped with practical tools and techniques to fine-tune your LLMs for optimal performance across various tasks and domains.
In this chapter, we’ll be covering the following topics:
- Understanding hyperparameters
- Manual versus automated tuning
- Grid and random search
- Bayesian optimization
- Population-based methods
- Multi-objective hyperparameter optimization
- Hyperparameter tuning at scale – challenges and solutions