Transformers & Scaled Dot-Product Self-Attention
Multi-Head Attention Mechanisms, Softmax Routing & Positional Encodings
#Transformers#Attention#Self-Attention#NLP#Foundation Models#Vaswani
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Treating the Transformer as a mysterious black box and attempting to tune prompt temperatures rather than understanding context limits and attention patterns.
Why It Fails in Production:
Engineers who don’t understand attention mechanisms fail to realize that context window quadratic memory ($O(N^2)$) causes GPU OOM crashes in production.
Targeted Algorithm (Transformers & Scaled Dot-Product Self-Attention)
Latency:Self-Attention: O(N^2 * d)
Cost / 1M Ops:GPU RAM Bound
Determinism:Exact Attention Weights
Generative LLM Alternative
Latency:Black Box Prompting
Cost / 1M Ops:High Cloud Bills
Determinism:Opaque Behaviour