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GLaM (Generalist Language Model) December 2021: Google: 1200 [35] 1.6 trillion tokens [35] 5600 [35] Proprietary Sparse mixture of experts model, making it more expensive to train but cheaper to run inference compared to GPT-3. Gopher: December 2021: DeepMind: 280 [36] 300 billion tokens [37] 5833 [38] Proprietary Later developed into the ...
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.
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 ]
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 ...
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data.
A slightly-modified version of the algorithm is used in large language model tokenizers. The original version of the algorithm focused on compression. It replaces the highest-frequency pair of bytes with a new byte that was not contained in the initial dataset. A lookup table of the replacements is required to rebuild the initial dataset. The ...