Understanding model customization
The principle behind fine-tuning and continued pre-training comes from the broad concept of transfer learning, which, as its name suggests, entails transferring knowledge that’s been acquired from one problem to other often related but distinct problems. This practice is widely employed in the field of machine learning (ML) to enhance the performance of models on new tasks or domains.
Model customization is a five-step process:
- Identify your use case and data: Identifying the use case/task and how it solves your organization’s business objectives is a critical step. Do you want to summarize legal documents, perform Q&A on medical reports, or do something else? Once you’ve identified the use case, you must gather enough relevant datasets that you can use for model customization. The dataset should contain examples that the model can learn intricate details from. Remember, how your custom model performs on your task...
