Early Transformer Milestones
The original Transformer architecture was introduced in the 2017 paper "Attention Is All You Need," establishing self-attention as a core mechanism for sequence modeling. Early variants expanded on this design, focusing on encoder-decoder setups for translation and text generation tasks.
Encoder-Only and Decoder-Only Models
Encoder-only models like BERT are optimized for understanding tasks such as classification and named entity recognition. Decoder-only models, including GPT series, are built for autoregressive generation and form the basis of most modern large language models.
Later Architectures and Scaling Trends
Subsequent models introduced sparse attention, mixture-of-experts, and retrieval augmentation to improve efficiency and knowledge grounding. Many of these developments are documented in technical reports and research summaries available on sites like the Hugging Face blog.
For a practical overview of model families and release order, you can explore the Hugging Face transformers library documentation.
