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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]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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.
OpenAI also makes GPT-4 available to a select group of applicants through their GPT-4 API waitlist; [239] after being accepted, an additional fee of US$0.03 per 1000 tokens in the initial text provided to the model ("prompt"), and US$0.06 per 1000 tokens that the model generates ("completion"), is charged for access to the version of the model ...
GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but was significantly worse than the largest GPT-3. [168] GPT-J: June 2021: EleutherAI: 6 [169] 825 GiB [167] 200 [170] Apache 2.0 GPT-3-style language model Megatron-Turing NLG: October 2021 [171] Microsoft and Nvidia: 530 [172] 338.6 billion tokens [172] 38000 [173 ...
The number of neurons in the middle layer is called intermediate size (GPT), [55] filter size (BERT), [35] or feedforward size (BERT). [35] It is typically larger than the embedding size. For example, in both GPT-2 series and BERT series, the intermediate size of a model is 4 times its embedding size: =.
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