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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 ...
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
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.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Meta AI (formerly Facebook) also has a generative transformer-based foundational large language model, known as LLaMA. [48] Foundational GPTs can also employ modalities other than text, for input and/or output. GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text). [49]
Amazon Web Services began hosting Common Crawl's archive through its Public Data Sets program in 2012. [9]The organization began releasing metadata files and the text output of the crawlers alongside .arc files in July 2012. [10]