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  2. GPT-4o - Wikipedia

    en.wikipedia.org/wiki/GPT-4o

    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]

  3. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    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]

  4. GPT-4 - Wikipedia

    en.wikipedia.org/wiki/GPT-4

    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]

  5. List of large language models - Wikipedia

    en.wikipedia.org/wiki/List_of_large_language_models

    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 ...

  6. These are the 2024 SEO trends you need to know - AOL

    www.aol.com/lifestyle/2024-seo-trends-know...

    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.

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    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.

  8. Gemini (language model) - Wikipedia

    en.wikipedia.org/wiki/Gemini_(language_model)

    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. [45] Gemma 2 27B is trained on web documents, code, science articles.

  9. llama.cpp - Wikipedia

    en.wikipedia.org/wiki/Llama.cpp

    This file format contains information necessary for running a GPT-like language model such as the tokenizer vocabulary, context length, tensor info and other ...