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Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5]
By 2027, AI may use up to 6.6 billion cubic meters of water. [31] One professor has estimated that an average session on ChatGPT, with 10–50 responses, can use up to a half-liter of fresh water. [22] [32] [33] Training GPT-3 may have used 700,000 liters of water, equivalent to the water footprint of manufacturing 320 Tesla EVs. [32]
GPT-2, a text generating model developed by OpenAI Topics referred to by the same term This disambiguation page lists articles associated with the same title formed as a letter–number combination.
GPT-2: GPT-1, but with modified normalization 1.5 billion WebText: 40 GB of text, 8 million documents, from 45 million webpages upvoted on Reddit. February 14, 2019 (initial/limited version) and November 5, 2019 (full version) [40] "tens of petaflop/s-day", [41] or 1.5e21 FLOP. [42] GPT-3: GPT-2, but with modification to allow larger scaling ...
For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very large generative model for musical audio that contains billions of parameters. [14]
This page was last edited on 12 July 2024, at 14:37 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...
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Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020.. Like its predecessor, GPT-2, it is a decoder-only [2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [3]