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  2. AutoGPT - Wikipedia

    en.wikipedia.org/wiki/AutoGPT

    AutoGPT can be used to develop software applications from scratch. [5] AutoGPT can also debug code and generate test cases. [ 9 ] Observers suggest that AutoGPT's ability to write, debug, test, and edit code may extend to AutoGPT's own source code, enabling self-improvement.

  3. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.

  4. OpenAI Codex - Wikipedia

    en.wikipedia.org/wiki/OpenAI_Codex

    It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim. [1] 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]

  5. GPT Store - Wikipedia

    en.wikipedia.org/wiki/GPT_Store

    The GPT Store is a platform developed by OpenAI that enables users and developers to create, publish, and monetize GPTs without requiring advanced programming skills. GPTs are custom applications built using the artificial intelligence chatbot known as ChatGPT .

  6. GitHub Copilot - Wikipedia

    en.wikipedia.org/wiki/GitHub_Copilot

    Copilot's OpenAI Codex was trained on a selection of the English language, public GitHub repositories, and other publicly available source code. [2] This includes a filtered dataset of 159 gigabytes of Python code sourced from 54 million public GitHub repositories. [15] OpenAI's GPT-3 is licensed exclusively to Microsoft, GitHub's parent ...

  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.

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  9. GPT-2 - Wikipedia

    en.wikipedia.org/wiki/GPT-2

    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]