Fine-tuning is the process of adapting a pre-trained LLM to perform a specific task or operate in a particular domain. This involves training the model on a smaller, task-specific dataset while retaining the general language understanding gained during pretraining. For example, fine-tuning a general LLM with medical data can create a model specialized for diagnosing diseases.
Fine-tuning allows developers to customize the model’s behavior and improve its performance on specialized tasks without the need for extensive training from scratch. It is particularly useful when the pre-trained model’s general knowledge is insufficient for domain-specific challenges.
The process is relatively fast and computationally efficient compared to pretraining, as it involves adjusting only a subset of the model’s parameters. Techniques like low-rank adaptation (LoRA) or parameter-efficient fine-tuning (PEFT) further optimize this process, making it accessible for developers with limited computational resources.