eval: default the [gMASK]<sop> prefix ON for GLM snapshots (#108)

Scoring raw completions without GLM's training-time prefix runs the model
out-of-distribution: scores drop and A/B sensitivity distorts (#108). Detect
GLM via config.json model_type and prepend automatically, with a stderr
notice. EVAL_PREFIX (including empty) still overrides for research use.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

c/tools/eval_glm.py

@@ -48,11 +48,24 @@ def load_docs(task, data_dir, limit, seed):
     random.Random(seed).shuffle(docs)
     return docs[:limit] if limit else docs
 
-def build_requests(tk, docs_by_task):
+def detect_prefix(snap):
+    """GLM sees [gMASK]<sop> at the start of every training sequence; scoring raw text
+    without it is out-of-distribution and silently depresses/distorts scores (#108).
+    Default the prefix ON for GLM snapshots; EVAL_PREFIX (even empty) overrides."""
+    if "EVAL_PREFIX" in os.environ: return os.environ["EVAL_PREFIX"]
+    try: mt = json.load(open(os.path.join(snap, "config.json"))).get("model_type", "")
+    except Exception: mt = ""
+    if "glm" in mt.lower():
+        print("[prefix] GLM snapshot: prepending [gMASK]<sop> to every context "
+              "(override with EVAL_PREFIX, disable with EVAL_PREFIX=)", file=sys.stderr)
+        return "[gMASK]<sop>"
+    return ""
+
+def build_requests(tk, docs_by_task, prefix=""):
     reqs, meta, perq = [], [], {}
     for t, docs in docs_by_task.items():
         for qi, d in enumerate(docs):
-            ctx, conts, gold = d["ctx"], d["choices"], int(d["gold"])
+            ctx, conts, gold = prefix + d["ctx"], d["choices"], int(d["gold"])
             ctx_ids = tk.encode(ctx).ids
             for oi, cont in enumerate(conts):
                 full = tk.encode(ctx + cont).ids
@@ -115,7 +128,7 @@ def main():
     docs_by_task = {t: load_docs(t, a.data, a.limit, a.seed) for t in tasks}
     for t, d in docs_by_task.items(): print(f"[{t}] {len(d)} questions", file=sys.stderr)
 
-    reqs, meta, perq = build_requests(tk, docs_by_task)
+    reqs, meta, perq = build_requests(tk, docs_by_task, detect_prefix(a.snap))
     print(f"total requests: {len(reqs)} (answer options)", file=sys.stderr)
     if a.dry:
         for r in reqs[:3]: print("  example request:", r[:80], "...", file=sys.stderr)