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d70361b | 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | """
Day 6 (Uday): export the best fine-tuned AdaptFormer checkpoint to
``models/adaptformer_delhi/``.
Copies a HuggingFace ``save_pretrained`` directory (from finetune runs) and
optionally writes ``best.pt`` (state_dict) for the plan deliverable name.
Usage:
python scripts/export_adaptformer_delhi.py
python scripts/export_adaptformer_delhi.py --src runs/day5_full/20260716_142643/best
"""
from __future__ import annotations
import argparse
import json
import shutil
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
DEFAULT_CANDIDATES = [
ROOT / "runs" / "finetune_v3",
ROOT / "runs" / "finetune_v2",
ROOT / "runs" / "finetune_fix",
ROOT / "runs" / "day5_full",
ROOT / "runs" / "finetune_adaptformer",
]
def _find_latest_best() -> Path | None:
found: list[Path] = []
for cand in DEFAULT_CANDIDATES:
if cand.name == "best" and cand.is_dir():
found.append(cand)
continue
if cand.is_dir():
found.extend([p for p in cand.glob("*/best") if p.is_dir()])
if not found:
return None
return sorted(found, key=lambda p: p.stat().st_mtime, reverse=True)[0]
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--src", default="", help="path to HF best/ directory")
parser.add_argument("--out", default="models/adaptformer_delhi")
args = parser.parse_args()
src = Path(args.src).resolve() if args.src else _find_latest_best()
if src is None or not src.is_dir():
raise SystemExit(
"No fine-tune checkpoint found. Run Day 5 training first or pass --src."
)
out_root = ROOT / args.out
best_dir = out_root / "best"
best_dir.mkdir(parents=True, exist_ok=True)
# Copy HF artifacts
for name in ("config.json", "model.safetensors", "pytorch_model.bin",
"preprocessor_config.json", "tokenizer_config.json",
"special_tokens_map.json"):
f = src / name
if f.is_file():
shutil.copy2(f, best_dir / name)
# Copy any remaining files (custom code modules if present)
for f in src.iterdir():
if f.is_file() and not (best_dir / f.name).exists():
shutil.copy2(f, best_dir / f.name)
meta = {
"source": str(src),
"exported_to": "models/adaptformer_delhi/best",
"load_via": "ADAPTFORMER_WEIGHTS=models/adaptformer_delhi/best",
}
# Optional best.pt state_dict for plan naming
try:
import torch
from transformers import AutoModel
model = AutoModel.from_pretrained(best_dir, trust_remote_code=True)
pt_path = out_root / "best.pt"
torch.save(model.state_dict(), pt_path)
meta["best_pt"] = str(pt_path.relative_to(ROOT))
print(f"Wrote {pt_path}")
except Exception as exc:
meta["best_pt_error"] = str(exc)
print(f"Warning: could not write best.pt ({exc}); HF dir still exported.")
(out_root / "export_meta.json").write_text(json.dumps(meta, indent=2), encoding="utf-8")
print(f"Exported AdaptFormer Delhi weights -> {best_dir}")
print("Set ADAPTFORMER_WEIGHTS=models/adaptformer_delhi/best to use in the app.")
if __name__ == "__main__":
main()
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