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That development led to the emergence of large language models such as BERT (2018) [28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series. [29]
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing , machine translation , and natural language generation and can be used as foundation models for other tasks. [ 62 ]
OpenAI o1 is a reflective generative pre-trained transformer (GPT). A preview of o1 was released by OpenAI on September 12, 2024. o1 spends time "thinking" before it answers, making it better at complex reasoning tasks, science and programming than GPT-4o. [1] The full version was released to ChatGPT users on December 5, 2024. [2]
For example, training of the GPT-2 (i.e. a 1.5-billion-parameters model) in 2019 cost $50,000, while training of the PaLM (i.e. a 540-billion-parameters model) in 2022 cost $8 million, and Megatron-Turing NLG 530B (in 2021) cost around $11 million. [56] For Transformer-based LLM, training cost is much higher than inference cost.
GPT-3's capacity is ten times larger than that of Microsoft's Turing NLG, the next largest NLP model known at the time. [12] Lambdalabs estimated a hypothetical cost of around $4.6 million US dollars and 355 years to train GPT-3 on a single GPU in 2020, [16] with lower actual training time by using more GPUs in parallel.
The number of neurons in the middle layer is called intermediate size (GPT), [56] filter size (BERT), [36] or feedforward size (BERT). [36] 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-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] GPT-2 was created as a "direct scale-up" of GPT-1 [6] with a ten-fold increase in both its parameter count and the size of its training dataset. [5]
Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications. OpenAI released an API for Codex in closed beta. [1] In March 2023, OpenAI shut down access to Codex. [2] Due to public appeals from researchers, OpenAI reversed course. [3] The Codex model can still be used by researchers of the OpenAI Research ...