Fine-tuning Large Language Models (LLMs) | w/ Example Code

Fine-tuning large language models (LLMs) enhances their performance for specific tasks. The process involves adjusting internal parameters of pre-trained models, like GPT-3, to improve output relevance. Techniques include self-supervised learning, supervised learning, and reinforcement learning. Fine-tuning can lead to smaller models outperforming larger ones, making it a cost-effective approach.