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  2. OpenAI Codex - Wikipedia

    en.wikipedia.org/wiki/OpenAI_Codex

    Based on GPT-3, a neural network trained on text, Codex was additionally trained on 159 gigabytes of Python code from 54 million GitHub repositories. [ 5 ] [ 6 ] A typical use case of Codex is for a user to type a comment, such as " //compute the moving average of an array for a given window size ", then use the AI to suggest a block of code ...

  3. 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.

  4. GPT-3 - Wikipedia

    en.wikipedia.org/wiki/GPT-3

    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 ]

  5. 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 ...

  6. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    GPT-3 in 2020 went a step further and as of 2024 is available only via API with no offering of downloading the model to execute locally. But it was the 2022 consumer-facing browser-based ChatGPT that captured the imaginations of the general population and caused some media hype and online buzz. [ 15 ]

  7. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    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]

  8. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    Suppose we have two transformer models like GPT-3 and GPT-3-small, both with a context window size of 512. To generate an entire context window autoregressively with greedy decoding with GPT-3, it must be run for 512 times, each time generating a token x 1 , x 2 , . . . , x 512 {\displaystyle x_{1},x_{2},...,x_{512}} , taking time 512 T GPT-3 ...

  9. Llama (language model) - Wikipedia

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

    Code Llama is a fine-tune of LLaMa 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. [29] Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data ...