Introduction
Fine-tuning Large Language Models (LLMs) involves the process of adapting pretrained models, such as GPT-4, to perform specific tasks or operate within a particular domain by training them on a smaller, task-specific dataset.
You can use this approach to tap into the general knowledge and language skills of LLMs. Fine-tuning LLMs boost their performance in tasks like sentiment analysis, text generation, or understanding specific domain languages.
Fine-tuning lets you create models that fit your specific needs. As a result, your model is more accurate and relevant and you save on computational resources and time compared to starting from scratch.