Architecture similarity
Each model is encoded as a feature vector — attention class, MoE layout, normalization, positional encoding, activation, dtype, plus log-scaled size and context. Cosine similarity between these vectors surfaces architectural siblings: models that made the same structural bets at different scales.
Method
Vectors are built from one-hot encoding of categorical choices (attention class, norm, position encoding, activation, dtype) plus normalized log-scale features (total parameters, context length, layers, hidden size). Similarity is cosine; it measures structural resemblance, not capability.
Similarity heatmap
Similarity values range from 0 (no shared categorical features) to 1 (identical profiles).
similarity > 0.93
Similarity values range from 0 (no shared categorical features) to 1 (identical profiles).