Fine-Tuning LLMs : Overview, Methods, and Best Practices

An example of fine-tuning an LLM would be training it on a specific dataset or task to improve its performance in that particular area. For instance, if you wanted the model to generate more accurate medical diagnoses, you could fine-tune it on a dataset of medical records and then test its performance on medical diagnosis tasks. This process helps the model specialize in a particular domain while retaining its general language understanding capabilities.

You should opt for fine-tuning LLMs when you need to adapt your model to specific custom datasets or domains. Besides that, fine-tuning LLMs is helpful when you have stringent data compliance requirements and have a limited labeled dataset.

Transfer learning involves adapting a pre-trained model to a new but related task. Fine-tuning is a type of transfer learning where the model is further trained on a new dataset with some or all of the pre-trained layers set to be updatable, allowing the model to adjust its weights to the new task.