Tuning Generative AI models

Introduction

Google Cloud recently introduced new generative AI capabilities with Palm 2 for text and chat, now accessible to everyone through Vertex AI. In this article, we explore four ways to fine-tune these models for optimal performance. From prompt tuning to fine-tuning, adjusting model parameters, and exploring parameter-efficient tuning, there are various techniques to customize generative AI models effectively.

To access these models in Generative AI Studio, users can interact with them through a chat interface and deploy them easily. Prompt tuning allows for customizing the foundation model without extensive retraining by guiding the model with natural language prompts. Fine-tuning involves further training the model on new data to cater to specific use cases, such as legal or medical vocabulary. Adjusting model parameters like temperature, top p, and top K helps improve response quality, while parameter-efficient tuning focuses on training a small subset of parameters to streamline the fine-tuning process.

Keywords

Generative AI, Palm 2, Vertex AI, model tuning, prompt tuning, fine-tuning, model parameters, parameter-efficient tuning.

FAQ

What is prompt tuning in generative AI models? Prompt tuning is an efficient, low-cost method of customizing the foundation model without retraining it. It involves guiding the model with natural language prompts to generate useful output.
How does fine-tuning impact generative AI models? Fine-tuning refers to further training the model on new data, resulting in changes to the model's weights. This process is beneficial for use cases that require specialized results, like legal or medical vocabulary.
What are model parameters in generative AI, and how do they affect the quality of responses? Model parameters like temperature, top p, and top K allow users to adjust the randomness of responses generated by the model. Fine-tuning these parameters can significantly improve the quality of generated responses for specific use cases.