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BofA Securities: While earnings momentum will stall in 2025, Post noted Meta sent an upbeat tone on an “exciting product roadmap” for Meta AI, Llama, Meta Coding agent, and Meta AI glasses and ...
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]
Mistral AI is a French artificial intelligence (AI) startup, headquartered in Paris.It specializes in open-weight large language models (LLMs). [1] [2] Founded in April 2023 by engineers formerly employed by Google DeepMind [3] and Meta Platforms, the company has gained prominence as an alternative to proprietary AI systems.
Meta announced its artificial intelligence model Llama 2 in July 2023, available for commercial use via partnerships with major cloud providers like Microsoft. It was the first project to be unveiled out of Meta's generative AI group after it was set up in February.
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]
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 Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]