Introduction to Fine-Tuning

Overview

Fine-tuning refers to the process of adjusting the parameters of a pretrained model on a specific dataset to enhance its performance on a specific task. In LLMs, fine-tuning is generally used to transform a foundation model into a specialized one for a particular use case.

Fine-tuning LLMs brings several key benefits. One of these is it sharpens the model’s skills for specific jobs, like answering medical questions or writing legal documents. Learning how to fine-tune models can help individuals adapt powerful LLMs for their specific use cases.

The need for fine-tuning

Fine-tuning is a crucial step in ensuring that the general-purpose foundation models are aligned with the specific needs of users. The first versions of GPT-3 were foundation models that had been trained on a ...