Get the latest tech news
Understanding Transformers Using a Minimal Example
Visualizing the internal state of a Transformer model
While Transformer implementations operate on multi-dimensional tensors for efficiency in order to handle batches of sequences and processing entire context windows in parallel, we can simplify our conceptual understanding. Unlike vast text corpora, this dataset features repetitive patterns and clear semantic links, making it easier to observe how the model learns specific connections. Furthermore, it uses tied word embeddings (the same matrix for input lookup and output prediction, also used in Google's Gemma), reducing parameters and linking input/output representations in the same vector space which is helpful for visualization.
Or read this on Hacker News
