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Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of autoregressive 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]
Llama Conservation status Domesticated Scientific classification Domain: Eukaryota Kingdom: Animalia Phylum: Chordata Class: Mammalia Order: Artiodactyla Family: Camelidae Genus: Lama Species: L. glama Binomial name Lama glama (Linnaeus, 1758) Domestic llama and alpaca range Synonyms Camelus glama Linnaeus, 1758 The llama (Lama glama) is a domesticated South American camelid, widely used as a ...
Llama 3.1 July 2024 Meta AI 405 15.6T tokens 440,000: Llama 3 license 405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs. [95] [96] DeepSeek V3 December 2024 DeepSeek: 671 14.8T tokens 44,000: DeepSeek License 2.788M hours on H800 GPUs. [97] Amazon Nova December 2024 Amazon: Unknown Unknown Unknown Proprietary
llama.cpp began development in March 2023 by Georgi Gerganov as an implementation of the Llama inference code in pure C/C++ with no dependencies. This improved performance on computers without GPU or other dedicated hardware, which was a goal of the project.
Benchmark tests showed it outperformed Llama 3.1 and Qwen 2.5 while matching GPT-4o and Claude 3.5 Sonnet. [ 5 ] [ 11 ] [ 12 ] [ 13 ] DeepSeek's optimization on limited resources highlighted potential limits of US sanctions on China's AI development.
The guanaco (/ ɡ w ɑː ˈ n ɑː k oʊ / ghwuah-NAH-koh; [3] Lama guanicoe) is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations.
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.
This page was last edited on 20 December 2024, at 02:10 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.