Understanding Transformer Input/Output with GPT-2
Published:Nov 30, 2025 11:58
•1 min read
•Zenn NLP
Analysis
This article aims to explain the inner workings of Transformers, specifically focusing on the input and output data structures, using OpenAI's GPT-2 model as a practical example. It promises a hands-on approach, guiding readers through the process of how text is processed and used to predict the "next word". The article also briefly introduces the origin of the Transformer architecture, highlighting its significance as a replacement for RNNs and its reliance on the Attention mechanism. The focus on practical implementation and data structures makes it potentially valuable for those seeking a deeper understanding of Transformers beyond the theoretical level.
Key Takeaways
- •Transformers use Attention mechanisms instead of RNNs.
- •GPT-2 can be used to understand Transformer input/output.
- •The article focuses on the data structures involved in text processing.
Reference
“"Attention Is All You Need"”