Google Cloud recently published an article exploring when to use supervised fine-tuning (SFT) for Gemini models. The article positions SFT as a powerful way to tailor these models for specific tasks, domains, or even stylistic nuances.
What I found particularly interesting was the focus on comparing SFT to other methods for optimizing model output, such as prompt engineering, in-context learning, and Retrieval Augmented Generation. Developers often wonder when to use SFT and how it compares to other options, and the article provides a helpful framework for decision-making.
The article also provides concrete examples of how SFT can be used to fine-tune Gemini models in Vertex AI. For instance, SFT could be used to fine-tune a model to summarize financial documents or provide legal advice. These examples help to illustrate the potential of SFT for real-world applications.
Overall, I found the article to be a valuable resource for anyone interested in learning more about SFT and how it can be used to fine-tune Gemini models. The article provides a comprehensive overview of SFT, including when to use it and how it compares to other methods, as well as practical examples. I highly recommend this article to anyone looking to leverage the full power of Gemini models.
