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The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. Transformers is a library produced by Hugging Face that supplies transformer-based architectures and pretrained models. [11]
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]
As of 2024, the largest and most capable models are all based on the transformer architecture. Some recent implementations are based on other architectures, such as recurrent neural network variants and Mamba (a state space model). [21] [22] [23]
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.
Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020.. Like its predecessor, GPT-2, it is a decoder-only [2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [3]
[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. T5 models are usually pretrained on a massive dataset of text and code, after which they can perform the text-based tasks that are similar to their pretrained tasks.
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [ 1 ] [ 2 ] It learns to represent text as a sequence of vectors using self-supervised learning .
Reduced-parameter model trained on more data. Used in the Sparrow bot. Often cited for its neural scaling law. PaLM (Pathways Language Model) April 2022: Google: 540 [43] 768 billion tokens [42] 29,250 [38] Proprietary Trained for ~60 days on ~6000 TPU v4 chips. [38] As of October 2024, it is the largest dense Transformer published.