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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]
Phrase structure rules as they are commonly employed result in a view of sentence structure that is constituency-based. Thus, grammars that employ phrase structure rules are constituency grammars (= phrase structure grammars), as opposed to dependency grammars, [4] which view sentence structure as dependency-based. What this means is that for ...
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.
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, a general-purpose tensor library. [4] Command-line tools are included with the library, [5] alongside a server with a simple web interface. [6] [7]
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.
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.
The Sentence in Written English: A Syntactic Study Based on an Analysis of Scientific Texts. Cambridge University Press. p. 352. ISBN 978-0-521-11395-3. Jespersen, Otto (1982). Growth and Structure of the English Language. Chicago and London: University of Chicago Press. p. 244. ISBN 0-226-39877-3. Jespersen, Otto (1992). Philosophy of Grammar.
The tool that he mainly relied on is a categorial grammar with functional application; in terms of recent formulations, it can be considered Minimalist syntax with Merge only. However, this approach does not make predictions for some examples with inverse scope (wide scope in object position).