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It can handle almost any natural language task, as well as some multimodal tasks, such as image captioning, style transfer, and visual reasoning.

It can handle advanced natural language tasks, such as text-to-speech, speech-to-text, translation, paraphrasing, and question answering.ĭavinci is the largest and most powerful model, with 175 billion parameters and 45TB of text data. It can handle more complex natural language tasks, such as reasoning, logic, arithmetic, and word analogy.Ĭurie is a very large model, with 13 billion parameters and 800GB of text data. It can handle basic natural language understanding and generation tasks, such as classification, sentiment analysis, summarization, and simple conversation.īabbage is a larger model, with 3 billion parameters and 300GB of text data. They differ in the number of parameters, the amount of data they were trained on, and the types of tasks they can perform.Īda is the smallest and simplest model, with 350 million parameters and 40GB of text data. They are composed of four variants: Ada, Babbage, Curie, and Davinci. There are four Generative Pre-trained Transformer (GPT) models currently available from OpenAI and Azure OpenAI. About available OpenAI and Azure OpenAI GPT models OpenAI and Azure OpenAI offer a variety of models that can be customized and controlled through parameters or options, and that can be applied and integrated to various domains and tasks.

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👆Summary generated by semantic plugin SummarizeSkill.SummarizeĪ model refers to a specific instance or version of an LLM AI, such as GPT-3 or Codex, that has been trained and fine-tuned on a large corpus of text or code (in the case of the Codex model), and that can be accessed and used through an API or a platform. It also mentions similar LLM AI models and compares models based on their number of parameters. The article provides a brief overview of GPT models, including their variants, how they work, and how they can be fine-tuned.

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