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Generative Pre-trained Transformer 3.5 (GPT-3.5) is a sub class of GPT-3 Models created by OpenAI in 2022. On March 15, 2022, OpenAI made available new versions of GPT-3 and Codex in its API with edit and insert capabilities under the names "text-davinci-002" and "code-davinci-002". [ 28 ]
OpenAI has released significant GPT foundation models that have been sequentially numbered, to comprise its "GPT-n" series. [10] Each of these was significantly more capable than the previous, due to increased size (number of trainable parameters) and training. The most recent of these, GPT-4o, was released in May 2024. [11]
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 ...
The next biggest model out there, as far as we're aware, is OpenAI's GPT-3, which uses a measly 175 billion parameters. Background: Language models are capable of performing a variety of functions ...
OpenAI invited safety and security researchers to apply for early access of these models until January 10, 2025. [3] There are two different models: o3 and o3-mini. [4] On January 31, 2025, OpenAI released o3-mini to all ChatGPT users (including free-tier) and some API users. o3-mini features three reasoning effort levels: low, medium and high ...
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]
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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]