This Generative AI course focuses on fine-tuning Large Language Models (LLMs) to adapt them for specific applications and datasets. Fine-tuning allows you to leverage pre-trained models and customize them for specialized tasks such as text generation, summarization, or question answering. The course begins with an overview of transfer learning and why fine-tuning is essential for efficiency and accuracy in AI projects.

You will explore techniques for preparing datasets, adjusting model parameters, and training models without overfitting. Practical examples cover instruction-tuning, reinforcement learning from human feedback (RLHF), and domain adaptation. Additionally, you will learn how to evaluate model performance using metrics like perplexity and BLEU score, and how to deploy your fine-tuned models for real-world applications. By the end of this course, you will be equipped with the knowledge and skills to effectively fine-tune LLMs and create AI models tailored to your specific needs, improving both their efficiency and relevance in generative AI tasks.