Size-class leaders: relative neighbourhoods (-20%/+25%) instead of a fixed ±20M window

leaderboard.py

@@ -9,7 +9,10 @@ import hashlib
 import html
 import math
 
-MIN_GAMES_GLOW = 3  # a leader needs a few ranked games, so one lucky win doesn't glow
+MIN_GAMES_GLOW = 2  # a leader needs more than one ranked game, so a single lucky win doesn't glow
+# Size classes are relative, not absolute: a model's neighbourhood runs from size/1.25 to size*1.25
+# (-20% to +25%). A fixed ±20M window lumped every model under 20M into one class, so only one tiny model could glow.
+SIZE_RATIO = 1.25
 
 _CDN = "https://cdn-avatars.huggingface.co/v1/production/uploads/"
 # owner on the Hub -> (display name, logo, "r, g, b", border colour). Same identities as the BananaMind leaderboard.
@@ -86,14 +89,20 @@ def _logo(org, cls="lb-logo") -> str:
     return f'<span class="{cls} lb-initial" aria-hidden="true">{_esc(org["name"][:1].upper())}</span>'
 
 
-def leaders(entries, gap: int) -> set:
-    """Size-class leaders: no rated model within ±gap parameters has a higher Elo.
-    The window is the same one ranked matches use, so every leader beat real peers."""
+def leaders(entries, gap: int = 0) -> set:
+    """Size-class leaders: the best Elo among rated models of similar relative size.
+
+    A model glows when (1) it has at least MIN_GAMES_GLOW ranked games, (2) its Elo is above the 1000 start,
+    (3) at least one other rated model sits in its neighbourhood [size/SIZE_RATIO, size*SIZE_RATIO], and
+    (4) none of those neighbours has a higher Elo. Ties glow together. `gap` is kept for API compatibility."""
     out = set()
     for e in entries:
-        if e.get("games", 0) < MIN_GAMES_GLOW or not e.get("params"):
+        p = e.get("params")
+        if not p or e.get("games", 0) < MIN_GAMES_GLOW or e["elo"] <= 1000:
             continue
-        peers = [o for o in entries if o.get("params") and abs(o["params"] - e["params"]) <= gap]
+        peers = [o for o in entries if o.get("params") and p / SIZE_RATIO <= o["params"] <= p * SIZE_RATIO]
+        if len(peers) < 2:
+            continue  # no neighbour to compare with
         if all(o["elo"] <= e["elo"] for o in peers):
             out.add(e["model"])
     return out
@@ -261,8 +270,8 @@ def leaderboard_html(entries, protocol: str, gap: int, gap_label: str) -> str:
     return (
         '<div class="lb-root">'
         f'<section class="lb-panel lb-chart-panel">{views}'
-        f'<div class="lb-footer"><span><i class="lb-rainbow-dot"></i>Size-class leader: best Elo within ±{gap_label} parameters '
-        f'(min. {MIN_GAMES_GLOW} ranked games)</span><span>{len(entries)} models · click a model to open it ↗</span></div></section>'
+        f'<div class="lb-footer"><span><i class="lb-rainbow-dot"></i>Size-class leader: best Elo among models from −20% to +25% of its size '
+        f'(min. {MIN_GAMES_GLOW} ranked games, Elo above 1000)</span><span>{len(entries)} models · click a model to open it ↗</span></div></section>'
         '<section class="lb-panel lb-param-panel"><div class="lb-heading"><div><h2>Elo vs. Parameters</h2>'
         '<p class="lb-muted">Model size on a logarithmic scale · higher and further left is better</p></div></div>'
         '<div class="lb-key"><span><i class="lb-quadrant-key"></i>Fewer parameters, higher Elo</span><span>┈ Pareto line</span></div>'