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  2. Llama (language model) - Wikipedia

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

    It was observed that the Llama 3 models showed that when a model is trained on data that is more than the "Chinchilla-optimal" amount, the performance continues to scale log-linearly. For example, the Chinchilla-optimal dataset for Llama 3 8B is 200 billion tokens, but performance continued to scale log-linearly to the 75-times larger dataset ...

  3. llama.cpp - Wikipedia

    en.wikipedia.org/wiki/Llama.cpp

    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.

  4. MMLU - Wikipedia

    en.wikipedia.org/wiki/MMLU

    The MMLU was released by Dan Hendrycks and a team of researchers in 2020 [3] and was designed to be more challenging than then-existing benchmarks such as General Language Understanding Evaluation (GLUE) on which new language models were achieving better-than-human accuracy.

  5. Generative artificial intelligence - Wikipedia

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

    [49] However, this assessment was contested by other scholars who maintained that generative AI remained "still far from reaching the benchmark of 'general human intelligence'" as of 2023. [50] Later in 2023, Meta released ImageBind , an AI model combining multiple modalities including text, images, video, thermal data, 3D data, audio, and ...

  6. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    Benchmarks can become outdated rapidly. Once a model attains near-perfect scores on a given benchmark, that benchmark ceases to serve as a meaningful indicator of progress. This phenomenon, known as "benchmark saturation," necessitates the development of more challenging and nuanced tasks to continue advancing LLM capabilities.

  7. 8b/10b encoding - Wikipedia

    en.wikipedia.org/wiki/8b/10b_encoding

    An 8b/10b code can be implemented in various ways with focus on different performance parameters. One implementation was designed by K. Odaka for the DAT digital audio recorder. [2] Kees Schouhamer Immink designed an 8b/10b code for the DCC audio recorder. [3] The IBM implementation was described in 1983 by Al Widmer and Peter Franaszek. [4] [5]

  8. Rugg/Feldman benchmarks - Wikipedia

    en.wikipedia.org/wiki/Rugg/Feldman_benchmarks

    The Rugg/Feldman benchmarks are a series of seven short BASIC programming language programs that are used to test the performance of BASIC implementations on various microcomputers. They were published by Tom Rugg and Phil Feldman in the June 1977 issue of the US computer magazine, Kilobaud .

  9. Language model - Wikipedia

    en.wikipedia.org/wiki/Language_model

    A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.