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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]
On September 23, 2024, to further the International Decade of Indigenous Languages, Hugging Face teamed up with Meta and UNESCO to launch a new online language translator [15] built on Meta's No Language Left Behind open-source AI model, enabling free text translation across 200 languages, including many low-resource languages.
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]
In their paper, Meta researchers also teased upcoming "multimodal" versions of the models due out later this year that layer image, video and speech capabilities on top of the core Llama 3 text model.
Llama 3 license 405B version took 31 million hours on H100-80GB, at 3.8E25 FLOPs. [97] [98] DeepSeek-V3: December 2024: DeepSeek: 671 14.8T tokens 56,000: DeepSeek License 2.788M hours on H800 GPUs. [99] Amazon Nova December 2024: Amazon: Unknown Unknown Unknown Proprietary Includes three models, Nova Micro, Nova Lite, and Nova Pro [100 ...
LLaMA models have also been turned multimodal using the tokenization method, to allow image inputs, [86] and video inputs. [ 87 ] GPT-4 can use both text and image as inputs [ 88 ] (although the vision component was not released to the public until GPT-4V [ 89 ] ); Google DeepMind 's Gemini is also multimodal. [ 90 ]
Hugging Face alone hosts over 90,000 derivatives freighted from the Llama foundation model. The scale of post-training and model customization is massive and can collectively demand orders of ...
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...