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GPT-4o has knowledge up to October 2023, [15] [16] but can access the Internet if up-to-date information is needed. It has a context length of 128k tokens [15] with an output token limit capped to 4,096, [16] and after a later update (gpt-4o-2024-08-06) to 16,384. [17]
Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model trained and created by OpenAI and the fourth in its series of GPT foundation models. [1] It was launched on March 14, 2023, [1] and made publicly available via the paid chatbot product ChatGPT Plus, via OpenAI's API, and via the free chatbot Microsoft Copilot. [2]
llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project.
Wordkraft is powered by OpenAI’s GPT-3, GPT-3.5, and GPT-4 algorithms. In this case, they have been fine-tuned to produce more accurate and relevant content for Wordkraft users.
Other models with large context windows includes Anthropic's Claude 2.1, with a context window of up to 200k tokens. [46] Note that this maximum refers to the number of input tokens and that the maximum number of output tokens differs from the input and is often smaller. For example, the GPT-4 Turbo model has a maximum output of 4096 tokens. [47]
A fine-tuned variant of GPT-3, termed GPT-3.5, was made available to the public through a web interface called ChatGPT in 2022. [22] 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 ...
The second generation of Gemini ("Gemini 1.5") has two models. Gemini 1.5 Pro is a multimodal sparse mixture-of-experts, with a context length in the millions, while Gemini 1.5 Flash is distilled from Gemini 1.5 Pro, with a context length above 2 million. [46] Gemma 2 27B is trained on web documents, code, science articles.
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.