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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]
OpenAI's GPT-n series Model Architecture Parameter count Training data Release date Training cost GPT-1: 12-level, 12-headed Transformer decoder (no encoder), followed by linear-softmax. 117 million BookCorpus: [39] 4.5 GB of text, from 7000 unpublished books of various genres. June 11, 2018 [9] 30 days on 8 P600 GPUs, or 1 petaFLOP/s-day. [9 ...
For example, the small (i.e. 117M parameter sized) GPT-2 model has had twelve attention heads and a context window of only 1k tokens. [44] In its medium version it has 345M parameters and contains 24 layers, each with 12 attention heads. For the training with gradient descent a batch size of 512 was utilized. [28]
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: =.
GPT-Neo: March 2021: EleutherAI: 2.7 [23] 825 GiB [24] MIT [25] The first of a series of free GPT-3 alternatives released by EleutherAI. GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but was significantly worse than the largest GPT-3. [25] GPT-J: June 2021: EleutherAI: 6 [26] 825 GiB [24] 200 [27] Apache 2.0 GPT-3 ...
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Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 model. [177] Like its predecessor, [ 167 ] the GPT-3 trained model was not immediately released to the public for concerns of possible abuse, although OpenAI planned to allow access through a paid cloud API after a ...
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