The Transformer architecture is made up of two core components: an encoder and a decoder. The encoder contains layers that process input data, like text and images, iteratively layer by layer.

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.

Learn With Jay. Transformers’ Encoder Architecture Explained — No Phd Needed! Posted: May 7, 2025 | Last updated: May 7, 2025. Finally understand how encoder blocks work in transformers, with ...

Mu is built on a transformer-based encoder-decoder architecture featuring 330 million token parameters, making the SLM a good ...