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Model: jiamingshan/AHA-L2A-Qwen3-1.7B-repro
Source: Original Platform

recipe/scripts/validate_release.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""Fail fast if a downloaded release is incomplete or has drifted."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from collections import Counter
+from pathlib import Path
+
+from datasets import load_from_disk
+
+
+RECIPE = Path(__file__).resolve().parents[1]
+REPO = RECIPE.parent
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for block in iter(lambda: handle.read(8 * 1024 * 1024), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def read_jsonl(path: Path) -> list[dict]:
+    return [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
+
+
+def main() -> None:
+    manifest = json.loads((RECIPE / "manifest.json").read_text())
+    checked = []
+    for relative, expected in manifest["files"].items():
+        path = (RECIPE / relative).resolve()
+        if not path.exists():
+            raise FileNotFoundError(path)
+        if path.name == "model.safetensors" and os.environ.get("SKIP_LARGE_HASH") == "1":
+            continue
+        actual = sha256(path)
+        if actual != expected:
+            raise RuntimeError(f"SHA256 mismatch for {path}: {actual} != {expected}")
+        checked.append(str(path.relative_to(REPO)))
+
+    config = json.loads((REPO / "config.json").read_text())
+    if config.get("model_type") != "qwen3":
+        raise RuntimeError(f"unexpected tuned-vanilla model_type: {config.get('model_type')}")
+    dataset = load_from_disk(str(RECIPE / "data/am_distilled_long_mix"))
+    if len(dataset["train"]) != 1024:
+        raise RuntimeError(f"expected 1024 training rows, found {len(dataset['train'])}")
+
+    helmet = read_jsonl(RECIPE / "data/eval_inputs/helmet_icl_8k_n50_per_config.jsonl")
+    mrcr = read_jsonl(RECIPE / "data/eval_inputs/mrcr_8k_2_4_8needle_n10_per_config.jsonl")
+    helmet_counts = Counter(row["config"] for row in helmet)
+    mrcr_counts = Counter(row["config"] for row in mrcr)
+    if sorted(helmet_counts.values()) != [50] * 5:
+        raise RuntimeError(f"unexpected HELMET counts: {helmet_counts}")
+    if sorted(mrcr_counts.values()) != [10] * 3:
+        raise RuntimeError(f"unexpected MRCR counts: {mrcr_counts}")
+
+    print(json.dumps({
+        "status": "ok",
+        "checked_sha256": checked,
+        "model_type": config["model_type"],
+        "training_rows": len(dataset["train"]),
+        "helmet_rows": len(helmet),
+        "mrcr_rows": len(mrcr),
+    }, indent=2))
+
+
+if __name__ == "__main__":
+    main()