A first-principles breakdown of Rotary Position Embedding (RoPE), why it works, and its PyTorch implementation.
Why 2D Rotations Solved Transformer Long-Context: A Mechanics-First Look at RoPE
Attention requires two things from positional encodings:
Relative Distance Sensitivity: Token
ii
attending to Token
We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a…
Self-Attention can compare every token with every other token. But there is a catch. By itself, it...
My RAG pipeline looked fine on paper. Fast retrieval. Decent cosine scores. But when I tested it with...
If you've built a RAG pipeline before, you know the pattern: hook up an embedding model, dump vectors...
Robotics Gets Its PyTorch Stack.
Originally published on my blog. Cross-posted here with a canonical link. Recap:...
