Editing Transformer Architecture (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert-level evaluation of transformer systems goes beyond benchmark scores:

'''Scaling law analysis''': Chinchilla scaling laws show that model size and training data should scale proportionally. Experts understand that a 70B model trained on too little data is less efficient than a 7B model with optimal training.

'''Attention pattern visualization''': Tools like BertViz allow visualization of attention heads. Experts use this to verify that heads are learning meaningful patterns (e.g., syntactic dependencies, coreference) rather than degenerate uniform attention.

'''Emergent capability tracking''': Some capabilities appear suddenly at scale thresholds (chain-of-thought reasoning emerges around 100B parameters). Experts track these phase transitions to understand capability vs. scale relationships.

'''KV-cache profiling''': For production deployment, the key-value cache is the dominant memory bottleneck. Expert engineers profile cache size, hit rates, and eviction strategies in serving infrastructure.
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