Model Fine-Tuning

This collection explores advanced techniques in refining machine learning models, specifically focusing on the fine-tuning of large language models and other algorithms. The content includes studies on applications ranging from health care predictions and sentiment analysis to enhancing marketing analytics. It covers key strategies such as data augmentation, prompt engineering, and retrieval-augmented generation, demonstrating how these methods can significantly improve model performance across various tasks and sectors.