This blog post discusses the application of large language models (LLMs) in hyperparameter optimization (HPO) for machine learning. It presents a methodology using LLMs to suggest and refine hyperparameter configurations based on model performance, showing that LLMs can match or exceed traditional methods like Bayesian optimization within budget constraints. The paper also introduces a novel approach treating model code as a hyperparameter, enhancing flexibility in HPO.