GPT

Overview Nucleus sampling is also known as Top_p Sampling.  In Nucleus sampling, the model only considers the tokens with the highest probability mass, which is determined by the top_p parameter. OpenAI recommends to use either one but not both: nucleus sampling or temperature sampling Top_p Sampling The Top_P parameter controls the diversity of the text […]

The GPT Temperature setting is an important setting that significantly impacts the model’s response. It controls the amount of randomness in the output

In this tutorial, we will learn how to set the Presence Penalty setting using OpenAI Playground or API requests. This setting increases the model’s probability

The frequency penalty setting suppresses or allows the model to repeat while generating the response. This tutorial will teach us how to set this setting using OpenAI