But not all transformer applications require both the encoder and decoder module. For example, the GPT family of large language models uses stacks of decoder modules to generate text.

In recent years, with the rapid development of large model technology, the Transformer architecture has gained widespread attention as its core cornerstone. This article will delve into the principles ...

For both encoder and decoder architectures, the core component is the attention layer, as this is what allows a model to retain context from words that appear much earlier in the text.

Seq2Seq is essentially an abstract deion of a class of problems, rather than a specific model architecture, just as the ...

An Encoder-decoder architecture in machine learning efficiently translates one sequence data form to another.

BLT architecture (source: arXiv) The encoder and decoder are lightweight models. The encoder takes in raw input bytes and creates the patch representations that are fed to the global transformer.

The LLM component of multimodal models has the same general transformer architecture. The connector in LLaVA is a straightforward matrix multiplication translating image features (the output from the ...