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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]
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 ...
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 GGUF (GGML Universal File) [30] file format is a binary format that stores both tensors and metadata in a single file, and is designed for fast saving, and loading of model data. [31] It was introduced in August 2023 by the llama.cpp project to better maintain backwards compatibility as support was added for other model architectures.
The new Llama 3 model can converse in eight languages, write higher-quality computer code and solve more complex math problems than previous versions, the Facebook parent company said in blog ...
Cost, safety, and the relentless pace of AI progress may temper the impact of Meta's new monster model. Meta’s new LLama model could be a game changer—but there are a lot of unknowns Skip to ...
Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5]
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.