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4791c0a 13fe947 4791c0a 13fe947 4791c0a | 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 98 99 100 101 102 103 104 105 106 | #!/usr/bin/env python3
"""Publish the trained quest-classification LoRA adapter to the Hub.
Uploads artifacts/quest-lora/ to a model repo and prints the commit revision to pin
in hackathon_advisor.quest_analysis (ADVISOR_QUEST_ADAPTER_REVISION).
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from huggingface_hub import HfApi
DEFAULT_REPO = "build-small-hackathon/hackathon-advisor-quest-minicpm5-lora"
def model_card(recipe: dict, eval_report: dict) -> str:
n = eval_report.get("n")
exact = eval_report.get("quest_set_exact")
f1 = eval_report.get("f1")
return "\n".join(
[
"---",
"base_model: openbmb/MiniCPM5-1B",
"library_name: peft",
"datasets:",
"- build-small-hackathon/hackathon-advisor-quest-dataset",
"tags:",
"- lora",
"- hackathon-advisor",
"- quest-classification",
"license: apache-2.0",
"---",
"",
"# Hackathon Advisor — Quest Classification LoRA (MiniCPM5-1B)",
"",
"PEFT LoRA adapter that classifies a Build Small Hackathon project against 13 judging",
"dimensions (6 merit badges + 2 tracks + 5 sponsor/special awards) from a two-segment",
"README + app-file prompt, emitting strict JSON:",
"",
"```json",
'{"matches":[{"quest":"...","confidence":0.0,"evidence":"...","source":"readme|app_file"}]}',
"```",
"",
"Load it in the deployed Space by setting `ADVISOR_QUEST_ADAPTER_ID` to this repo.",
"The backend revalidates every dashboard refresh and will not swap on schema failure.",
"",
"## Recipe",
"",
f"- Base model: `{recipe.get('base_model')}`",
f"- Task: `{recipe.get('adapter_task')}`",
f"- Method: {recipe.get('method')}",
f"- Examples: {recipe.get('example_count')}",
f"- Epochs: {recipe.get('epochs')}",
f"- LoRA rank/alpha/dropout: {recipe.get('rank')}/{recipe.get('alpha')}/{recipe.get('dropout')}",
f"- Max seq length: {recipe.get('max_seq_length')}",
f"- GPU: {recipe.get('gpu')}",
"",
"## Dataset",
"",
"[`build-small-hackathon/hackathon-advisor-quest-dataset`]"
"(https://huggingface.co/datasets/build-small-hackathon/hackathon-advisor-quest-dataset)"
" — 156 chat-JSONL examples built from real `build-small-hackathon` Spaces: 108 teacher-",
"labelled + adversarially-verified projects plus targeted augmentations (app-only,",
"readme-only / missing app file, README↔app contradictions, empty matches, noisy",
"metadata). All 13 quests covered.",
"",
f"## Full-dataset eval at training time: quest-set exact match {exact}/{n}, micro-F1 {f1}.",
"",
"Evaluated by reproducing the gold quest set for every example in the training dataset",
"(the dataset is the spec — it is built from the real `build-small-hackathon` projects).",
"",
]
)
def main() -> None:
parser = argparse.ArgumentParser(description="Publish the quest LoRA adapter.")
parser.add_argument("--adapter-dir", default="artifacts/quest-lora", type=Path)
parser.add_argument("--repo-id", default=DEFAULT_REPO)
args = parser.parse_args()
recipe_path = args.adapter_dir / "training-recipe.json"
recipe = json.loads(recipe_path.read_text(encoding="utf-8")) if recipe_path.exists() else {}
eval_path = args.adapter_dir / "self-eval.json"
eval_report = json.loads(eval_path.read_text(encoding="utf-8")) if eval_path.exists() else {}
(args.adapter_dir / "README.md").write_text(model_card(recipe, eval_report), encoding="utf-8")
api = HfApi()
api.create_repo(repo_id=args.repo_id, repo_type="model", exist_ok=True)
commit = api.upload_folder(
folder_path=str(args.adapter_dir),
repo_id=args.repo_id,
repo_type="model",
commit_message="Publish Hackathon Advisor quest-classification MiniCPM5 LoRA",
ignore_patterns=["self-eval.json"],
)
revision = getattr(commit, "oid", None) or getattr(commit, "commit_id", None) or str(commit)
print(f"published {args.repo_id}")
print(f"revision: {revision}")
if __name__ == "__main__":
main()
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