File size: 2,573 Bytes
46b9eea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | #!/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(encoding="utf-8").splitlines() if line.strip()]
def main() -> None:
manifest = json.loads((RECIPE / "manifest.json").read_text(encoding="utf-8"))
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(encoding="utf-8"))
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()
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