add eval_router_only.py

eval_router_only.py

@@ -0,0 +1,34 @@
+"""© KAND CA 2026 - evaluate a router checkpoint without retraining.
+
+Reuses train_router_head.py's own loaders and report() so a published model and
+a new one are scored by identical code in one session.
+
+A checkpoint saved by train_router_head.py is a directory with the backbone plus
+router_model.pt (the span scorer). The published Nawah-Router-v3 has the same
+layout, so both load through the same path.
+"""
+import os, sys
+os.environ.setdefault("DATA_DIR", "ds/data")
+import torch
+from transformers import AutoTokenizer
+import train_router_head as T
+
+def load_model(mid):
+    m = T.RouterModel(mid)
+    w = os.path.join(mid, "router_model.pt") if os.path.isdir(mid) else None
+    if w is None:
+        from huggingface_hub import hf_hub_download
+        w = hf_hub_download(mid, "router_model.pt")
+    if os.path.exists(w):
+        m.load_state_dict(torch.load(w, map_location="cpu", weights_only=True))
+    return m.cuda().eval()
+
+for mid in sys.argv[1:]:
+    tok = AutoTokenizer.from_pretrained(mid)
+    m = load_model(mid)
+    n = sum(p.numel() for p in m.parameters())
+    print(f"\n{'='*66}\n{mid}   params={n:,}")
+    for name in ("eval_unseen_lanes", "eval_unseen_domain", "eval_unseen_axis", "eval_hard"):
+        rows = T.load(name)
+        T.report(m, tok, rows, name.replace("eval_", ""))
+    del m; torch.cuda.empty_cache()