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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]
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 Llama in question wasn’t an animal: Llama 2 was the follow-up release of Meta’s generative AI model—a would-be challenger to OpenAI’s GPT-4. The first Llama had come out a few months ...
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]
The model scored 88.6 on MMLU, a benchmark that covers dozens of subjects across math, science and the humanities, while GPT-4o scored 88.7 and Claude 3.5 Sonnet scored 88.3.
X-bar theory graph of the sentence "He studies linguistics at the university." Constituency is a one-to-one-or-more relation; every word in the sentence corresponds to one or more nodes in the tree diagram. Dependency, in contrast, is a one-to-one relation; every word in the sentence corresponds to exactly one node in the tree diagram.
A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are often examples of foundation models.
The deductive argument is called an explanation, its premisses are called the explanans (L: explaining) and the conclusion is called the explanandum (L: to be explained). Depending on a number of additional qualifications, an explanation may be ranked on a scale from potential to true. Not all explanations in science are of the D-N type, however.