Heatmap: First the larger models, then the smaller ones

server.py

@@ -840,8 +840,8 @@ class LeaderboardServer:
         sorted_indices = sizes_series.sort_values(ascending=False).index
         original_scores = original_scores.loc[sorted_indices]  # Sort rows by model size
         
-        # Smaller models
-        original_scores_sub = original_scores[sizes_series < 16]
+        # Bigger models
+        original_scores_sub = original_scores[sizes_series >= 16]
 
         # Apply quantile transformation independently for each row
         normalized_scores_sub = original_scores_sub.apply(lambda x: (x - x.min()) / (x.max() - x.min()), axis=0)
@@ -850,12 +850,12 @@ class LeaderboardServer:
         p1 = create_heatmap(
             normalized_scores_sub,
             original_scores_sub * 100,
-            x_axis_label="Model <16B",
+            x_axis_label="Model ≥16B",
             y_axis_label=fig_y_axis_label,
         )
         
-        # Bigger models
-        original_scores_sub = original_scores[sizes_series >= 16]
+        # Smaller models
+        original_scores_sub = original_scores[sizes_series < 16]
 
         # Apply quantile transformation independently for each row
         normalized_scores_sub = original_scores_sub.apply(lambda x: (x - x.min()) / (x.max() - x.min()), axis=0)
@@ -864,7 +864,7 @@ class LeaderboardServer:
         p2 = create_heatmap(
             normalized_scores_sub,
             original_scores_sub * 100,
-            x_axis_label="Model ≥16B",
+            x_axis_label="Model <16B",
             y_axis_label=fig_y_axis_label,
             y_axis_visible=False,
         )