In this Issue:

1. An overview of the different methods for fine-tuning foundation models.

2. A review of MIT’s research about multi-task prompting.

3. An introduction to Lamini’s platform for fine tuning LLMs.

💡 ML Concept of the Day: Types of Fine-Tuning Methods

The research about fine-tuning methods is in relatively early stages but moving quite fast. To understand fine-tuning techniques, sometimes it is important to maintain a simple taxonomy that helps fits the different methods. Conceptually, we can link fine-tuning to a variation of transfer learning methods that focus on transferring knowledge from one model to another. In this case, we are talking about transferring knowledge from the base model to the fine-tuned version...