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Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of 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]
On July 18, 2023, Meta released Llama 2 “free for research and commercial use.” In a post on his personal Facebook page, Zuckerberg doubled down on his decision.
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 ...
The game uses Llama 2.7 and Llama 3.1, a large language model, to create new elements and assign emojis. [ 1 ] [ 3 ] [ 4 ] When a player combines two elements on the website, the game checks from its database if these two elements have already been combined before—if they have not, the generative AI creates a new element which is then saved ...
DeepSeek [a] (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence company that develops open-source large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the company in 2023 and serves as its CEO.
The following examples are taken from the "Abstract Algebra" and "International Law" tasks, respectively. [3]The correct answers are marked in boldface: Find all in such that [] / (+) is a field.
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.
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.