docs: Jev candidate bench vs SemIf (fv-ml1 GPU 3) — Intern-Decision-4B is the replacement if SemIf is displaced

Operator ask relayed by brokkr-smithy-dev. Positive control (SemIf 187/231, hard 0.613) reproduced exactly; negative control and a 4-restart noise floor (0 flips) measured. On our replaced-baseline sets no candidate beats SemIf-with- rotations beyond the ~4-pt floor; Intern-Decision-4B native matches it at one ordering, is better on Wyrd, fits 9.7/10.3 GB and is 1.5-2.3x faster. JevBench rank does not transfer. Raw per-item data kept out of git.
2026-09-30 05:00:45 -07:00
parent 9a6ac59da7
commit 475d6d6bcb
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+"""A TypeSafe-style POST /v1/systemone server around Intern-Decision's own inference.py
+(DecisionEngine.predict, shipped in the model repo), so JevBench's typesafe adapter and the bench
+harness reach it the same way they reach jevk5-serve and imajev's server. Nothing about the scoring
+is changed: the request goes to predict() as-is (the card's documented request format) and its
+response is returned with one added field, timing.server_ms.
+    python intern_server.py --checkpoint <snapshot dir> --port 18090
+"""
+import argparse
+import json
+import sys
+import threading
+import time
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--checkpoint", required=True)
+ap.add_argument("--host", default="127.0.0.1")
+ap.add_argument("--port", type=int, default=18090)
+args = ap.parse_args()
+sys.path.insert(0, args.checkpoint)
+from inference import DecisionEngine  # noqa: E402  (the checkpoint's own module)
+
+engine = DecisionEngine(checkpoint=args.checkpoint, device="cuda")
+engine.predict({"state": "warm-up", "questions": {"q": {"type": "noul", "instructions": "Is this a warm-up?"}}})
+lock = threading.Lock()
+
+
+class H(BaseHTTPRequestHandler):
+    def _send(self, code, payload):
+        data = json.dumps(payload).encode()
+        self.send_response(code)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(data)))
+        self.end_headers()
+        self.wfile.write(data)
+
+    def do_GET(self):
+        self._send(200, {"ok": True, "model": "Intern-Decision"} if self.path.rstrip("/") == "/health"
+                   else {"error": "not found"})
+
+    def do_POST(self):
+        if self.path.rstrip("/") != "/v1/systemone":
+            return self._send(404, {"error": "not found"})
+        t = time.perf_counter()
+        try:
+            body = json.loads(self.rfile.read(int(self.headers.get("Content-Length", 0))))
+            with lock:
+                out = engine.predict({k: body[k] for k in ("state", "questions", "images") if k in body})
+        except (ValueError, KeyError, TypeError, ArithmeticError) as e:
+            return self._send(400, {"error": f"{type(e).__name__}: {e}"})
+        except Exception as e:  # noqa: BLE001  e.g. CUDA OOM under the cap
+            import torch
+            torch.cuda.empty_cache()
+            return self._send(503, {"error": f"{type(e).__name__}: {str(e)[:300]}"})
+        out.setdefault("timing", {})["server_ms"] = round((time.perf_counter() - t) * 1000, 2)
+        self._send(200, out)
+
+    def log_message(self, *a):
+        pass
+
+
+print(f"serving Intern-Decision {args.checkpoint} on {args.host}:{args.port}", flush=True)
+ThreadingHTTPServer((args.host, args.port), H).serve_forever()