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One encoder-decoder block A Transformer is composed of stacked encoder layers and decoder layers. Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding ...
Code Llama is a fine-tune of LLaMa 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. [29] Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data ...
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 ...
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.
First GPT model, decoder-only transformer. Trained for 30 days on 8 P600 GPUs. BERT: October 2018: Google: 0.340 [3] 3.3 billion words [3] 9 [4] Apache 2.0 [5] An early and influential language model. [6] Encoder-only and thus not built to be prompted or generative. [7] Training took 4 days on 64 TPUv2 chips. [8] T5: October 2019: Google 11 [9 ...
The idea of encoder-decoder sequence transduction had been developed in the early 2010s (see previous papers [20] [21]). The papers most commonly cited as the originators that produced seq2seq are two concurrently published papers from 2014. [20] [21] A 380M-parameter model for machine translation uses two long short-term memories (LSTM). [21]
Though the original transformer has both encoder and decoder blocks, BERT is an encoder-only model. Academic and research usage of BERT began to decline in 2023, following rapid improvements in the abilities of decoder-only models (such as GPT) to solve tasks via prompting. [13]
The architectures for which the scaling behaviors of artificial neural networks were found to follow this functional form include residual neural networks, transformers, MLPs, MLP-mixers, recurrent neural networks, convolutional neural networks, graph neural networks, U-nets, encoder-decoder (and encoder-only) (and decoder-only) models ...