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

Fine-tuning LLMs Hero

Frequently Asked Questions

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.

View more FAQs


What’s up with Turing? Get the latest news about us here.


Know more about remote work. Checkout our blog here.


Have any questions? We’d love to hear from you.

Hire remote developers

Tell us the skills you need and we'll find the best developer for you in days, not weeks.