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| |
| |
| """Evaluate a base model or LoRA candidate on a frozen curated split.""" |
|
|
| from __future__ import annotations |
|
|
| from datetime import UTC, datetime |
| import hashlib |
| import json |
| import os |
| import re |
|
|
| from datasets import load_dataset |
| from huggingface_hub import HfApi |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
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|
|
| def _answer(text: str) -> str: |
| matches = re.findall(r"[-+]?\d+(?:\.\d+)?(?:/\d+)?", text.replace(",", "")) |
| return matches[-1] if matches else text.strip().casefold()[-200:] |
|
|
|
|
| def main() -> None: |
| model_repo = os.environ["MODEL_REPO"] |
| base_repo = os.getenv("BASE_MODEL_REPO") |
| revision = os.getenv("MODEL_REVISION") or None |
| dataset_repo = os.environ["DATASET_REPO"] |
| split = os.getenv("DATASET_SPLIT", "test") |
| max_cases = int(os.getenv("MAX_CASES", "500")) |
|
|
| tokenizer = AutoTokenizer.from_pretrained(base_repo or model_repo, revision=revision, use_fast=True) |
| model = AutoModelForCausalLM.from_pretrained(base_repo or model_repo, revision=revision, torch_dtype="auto", device_map="auto") |
| if base_repo: |
| from peft import PeftModel |
| model = PeftModel.from_pretrained(model, model_repo) |
| data = load_dataset(dataset_repo, split=split, streaming=True) |
| cases, passed, unsafe = [], 0, 0 |
| for row in data.take(max_cases): |
| prompt = f"Solve the task. Return a clear final answer.\n\n{row['input']}" |
| encoded = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096).to(model.device) |
| with torch.inference_mode(): |
| output = model.generate(**encoded, max_new_tokens=1024, do_sample=False) |
| text = tokenizer.decode(output[0][encoded["input_ids"].shape[1]:], skip_special_tokens=True) |
| reference = str((row.get("evaluation") or {}).get("reference_answer") or "") |
| ok = bool(reference) and _answer(reference) == _answer(text) |
| is_unsafe = any(token in text for token in ("hf_", "sk-")) |
| passed += ok |
| unsafe += is_unsafe |
| cases.append({"example_id": row["example_id"], "passed": ok, "unsafe": is_unsafe, "output_sha256": hashlib.sha256(text.encode()).hexdigest()}) |
| report = { |
| "schema_version": "1.0.0", "created_at": datetime.now(UTC).isoformat(), |
| "model": {"repo_id": model_repo, "base_repo_id": base_repo, "revision": revision}, |
| "dataset": {"repo_id": dataset_repo, "split": split}, |
| "metrics": {"case_count": len(cases), "pass_rate": passed / len(cases) if cases else 0.0, "unsafe_rate": unsafe / len(cases) if cases else 0.0}, |
| "cases": cases, |
| } |
| payload = json.dumps(report, sort_keys=True, indent=2) + "\n" |
| HfApi().create_repo(os.environ["REPORT_REPO"], repo_type="dataset", private=True, exist_ok=True) |
| HfApi().upload_file(path_or_fileobj=payload.encode(), path_in_repo=os.environ["REPORT_PATH"], repo_id=os.environ["REPORT_REPO"], repo_type="dataset", commit_message=f"Evaluate {model_repo}") |
| print(payload) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
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|
