Azure OpenAI Playground
Overview of language models and hyperparameters
OpenAI and its disruptive development of generative models are probably the most trending topic in the Tech industry today. With good reason.
The company indeed aims to promote and develop friendly AI in a way that benefits humanity as a whole. OpenAI also provides a platform for training and deploying AI models, as well as offering pre-trained models that can be fine-tuned for specific tasks.
It focuses on generative AI models, like GPT-3 (language generation) or DALL-E (image generation based on natural language input).
In this article, we are going to focus on conversational AI models and how to consume them via API directly from OpenAI. We will also dwell on how to tune hyperparameters to tailor your models to your use case and needs.
In order to proceed, we first need an OpenAI playground. For this purpose, I will use my OpenAI instance on Azure and I’ll provide a step-by-step implementation. Note that you can also use the OpenAI playground available here.
The idea is that once satisfied with the models’ results in the playgrounds, those models can be called via API and embedded within your applications to make them AI-ready.
