normalization.yaml

# SAE input normalization statistics.
# Computed on the Stanford source TRAIN split (Data/vector_database/all_25_01_21)
# per get_data.py: mean is per-dimension, std is a scalar over all elements,
# dim_scale = sqrt(2048/dim). Applied as ((x - mean)/std)*dim_scale before the SAE.
findings:
  dim: 4096
  dim_scale: 0.7071067811865476
  mean: [0.010101629421114922, 0.023022262379527092, 0.007158663123846054, 0.008961183950304985,
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