1ïļâĢ The Myth:
People visualize attention maps thinking they show what the model âfocusesâ on.
But high attention â high importance.
Attention â Explanation.
ð§ĩ1/ What is RoPE (Rotary Position Embedding)?
RoPE (Su et al., 2021) replaces traditional positional encodings with rotations in complex space.
It encodes token positions by rotating query/key vectors, preserving relative positions elegantly.
Transformers choke on long sequences: full attention = O(nÂē) cost ðĩâðŦ
Enter Sparse Attention â smarter patterns that make attention linear or near-linear.
Letâs unpack the math, intuition & tradeoffs ð§ ð
What if you could fine-tune a billion-parameter model by training only 0.1% of its weights?
Thatâs the magic of LoRA (Low-Rank Adaptation). Letâs dive deep into the math, intuition, and why it works. ðð§ĩ
