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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 (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 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 ]
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]
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 ...
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.
Byte pair encoding [1] [2] (also known as BPE, or digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into smaller strings by creating and using a translation table. [4] A slightly-modified version of the algorithm is used in large language model tokenizers.