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.

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.

Discover the key differences between Moshi and Whisper speech-to-text models. Speed, accuracy, and use cases explained for your next project.

A Solution: Encoder-Decoder Separation The key to addressing these challenges lies in separating the encoder and decoder components of multimodal machine learning models.

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.