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Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. [2] [3] The latest version is Llama 3.3, released in December 2024. [4] Llama models are trained at different parameter sizes, ranging between 1B and 405B. [5]
llama.cpp is an open source software library that performs inference on various large language models such as Llama. [3] It is co-developed alongside the GGML project ...
Outperforms GPT-3.5 and Llama 2 70B on many benchmarks. [82] Mixture of experts model, with 12.9 billion parameters activated per token. [83] Mixtral 8x22B April 2024: Mistral AI: 141 Unknown Unknown: Apache 2.0 [84] Phi-2: December 2023: Microsoft 2.7 1.4T tokens 419 [85] MIT Trained on real and synthetic "textbook-quality" data, for 14 days ...
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
Provides warnings if tagged parameters do not match code, parsed parameters included in XML output and Doxygen-style tagfile (-D flag in 8.7). Partial C preprocessor support with -p flag. Support for #if/#ifdef control over documentation inclusion using the -D and -U command-line flags. Imagix 4D: customizable through style sheets and CSS
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.
For example, people may include spurious metadata into Web pages in an attempt to mislead Semantic Web engines that naively assume the metadata's veracity. This phenomenon was well known with metatags that fooled the Altavista ranking algorithm into elevating the ranking of certain Web pages: the Google indexing engine specifically looks for ...