| |
| """ |
| Assemble the Hugging Face bucket and publish it. |
| |
| python code/upload_bucket.py --repo adeerkhan/msq-plurality-gap [--dry-run] |
| |
| Stages, in order: |
| 1. dataset/ the 42 items (jsonl + csv + card) |
| 2. code/ the full harness |
| 3. results/ every generation and every metric |
| 4. figures/ the figures used in the README |
| 5. model/ the evaluated model's weights (openbmb/MiniCPM5-2B, Apache-2.0) |
| 6. README.md / FINDINGS.md / LICENSE / requirements.txt |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import os |
| import pathlib |
| import shutil |
| import sys |
|
|
| ROOT = pathlib.Path(__file__).resolve().parent.parent |
| STAGE = ROOT / ".bucket" |
|
|
| MODEL_REPO = "openbmb/MiniCPM5-2B" |
| MODEL_FILES = ["config.json", "generation_config.json", "model.safetensors", |
| "model.safetensors.index.json", "tokenizer.json", |
| "tokenizer_config.json", "special_tokens_map.json", |
| "chat_template.jinja"] |
|
|
| README_TEXT = """# MiniCPM5-2B (evaluated checkpoint) |
| |
| Unmodified weights of [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B), |
| mirrored here so the bucket is self-contained for re-running the evaluation. |
| |
| * Architecture: `LlamaForCausalLM`, 42 layers, GQA 16 Q / 2 KV heads |
| * Parameters: 2,516,756,480 (1,981,982,720 non-embedding) |
| * Context length: 131,072 |
| * Licence: Apache-2.0 |
| * SHA of the upstream revision this was taken from: see `UPSTREAM_REVISION.txt` |
| |
| The evaluation in this bucket was run against these weights in bfloat16 (and, |
| for the cross-model comparison, in NF4 4-bit for every model so that the |
| protocol is identical across models). The weights here are the unmodified |
| upstream bf16 checkpoint. |
| """ |
|
|
|
|
| def stage_model() -> None: |
| from huggingface_hub import snapshot_download |
| print("resolving model snapshot ...") |
| snap = pathlib.Path(snapshot_download(MODEL_REPO, |
| allow_patterns=MODEL_FILES + ["*.md"])) |
| dst = STAGE / "model" |
| dst.mkdir(parents=True, exist_ok=True) |
| for f in sorted(snap.iterdir()): |
| if f.name.endswith(".md"): |
| continue |
| target = dst / f.name |
| if target.exists(): |
| continue |
| shutil.copyfile(f.resolve(), target) |
| print(f" + model/{f.name} ({target.stat().st_size/1e6:.0f} MB)") |
| |
| from huggingface_hub import HfApi |
| info = HfApi().model_info(MODEL_REPO) |
| (dst / "UPSTREAM_REVISION.txt").write_text( |
| f"upstream repo : https://huggingface.co/{MODEL_REPO}\n" |
| f"revision sha : {info.sha}\n" |
| f"last modified : {info.last_modified}\n" |
| f"licence : {info.card_data.get('license') if info.card_data else 'apache-2.0'}\n") |
| (dst / "README.md").write_text(README_TEXT) |
|
|
|
|
| EXCLUDE_CODE = {"stop_all.sh", "stop_fetches.sh", "stop_runs.sh", "fetch_one.sh", |
| "__pycache__"} |
|
|
|
|
| def stage_tree() -> None: |
| STAGE.mkdir(parents=True, exist_ok=True) |
| for name in ("dataset", "code", "results", "figures"): |
| src = ROOT / name |
| if not src.exists(): |
| continue |
| dst = STAGE / name |
| if dst.exists(): |
| shutil.rmtree(dst) |
| shutil.copytree(src, dst, ignore=shutil.ignore_patterns("__pycache__", "*.pyc")) |
| if name == "code": |
| for f in EXCLUDE_CODE: |
| (dst / f).unlink(missing_ok=True) |
| print(f" + {name}/") |
| for f in ("README.md", "FINDINGS.md", "LICENSE", "requirements.txt"): |
| if (ROOT / f).exists(): |
| shutil.copyfile(ROOT / f, STAGE / f) |
| print(f" + {f}") |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--repo", default="adeerkhan/msq-plurality-gap") |
| ap.add_argument("--dry-run", action="store_true") |
| ap.add_argument("--skip-model", action="store_true") |
| ap.add_argument("--no-stage", action="store_true", |
| help="reuse the already-staged tree (for retries)") |
| args = ap.parse_args() |
|
|
| if STAGE.exists() and args.no_stage: |
| print(f"reusing staged tree at {STAGE}") |
| else: |
| if STAGE.exists(): |
| shutil.rmtree(STAGE) |
| print("staging tree ...") |
| stage_tree() |
| if not args.skip_model: |
| stage_model() |
|
|
| total = sum(p.stat().st_size for p in STAGE.rglob("*") if p.is_file()) |
| print(f"staged {total/1e9:.2f} GB at {STAGE}") |
|
|
| if args.dry_run: |
| for p in sorted(STAGE.rglob("*")): |
| if p.is_file(): |
| print(f" {p.relative_to(STAGE)} {p.stat().st_size/1e6:.1f} MB") |
| return |
|
|
| from huggingface_hub import HfApi, upload_folder |
| api = HfApi() |
| api.create_repo(repo_id=args.repo, repo_type="model", exist_ok=True, |
| private=False) |
| print(f"uploading to https://huggingface.co/{args.repo}") |
| upload_folder(repo_id=args.repo, repo_type="model", |
| folder_path=str(STAGE), commit_message="MSQ: the unstated premise") |
| print("done") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
