Building LLMs for Production

Understanding Transformers

The foundation that makes language models powerful lies in the transformer architecture. Transformer-based models addressed the challenges with RNNs and became the preferred architecture for the latest generation of LLMs. The original transformer network was presented as an encoder-decoder architecture for translation tasks. The next evolution of the architecture began with the introduction of encoder-only models like BERT in 2018, followed by the introduction of decoder-only networks in the first iteration of the GPT models.

The differences between encoder-only and decoder-only models extend beyond just network design and encompass the learning objectives. These models have contrasting learning objectives that are ...