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| import json | |
| import os | |
| import re | |
| from collections import defaultdict | |
| from datetime import datetime, timedelta, timezone | |
| import huggingface_hub | |
| from huggingface_hub import ModelCard | |
| from huggingface_hub.hf_api import ModelInfo, get_safetensors_metadata | |
| from transformers import AutoConfig, AutoTokenizer | |
| from src.envs import HAS_HIGHER_RATE_LIMIT | |
| # ht to @Wauplin, thank you for the snippet! | |
| # See https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/317 | |
| def check_model_card(repo_id: str) -> tuple[bool, str]: | |
| # Returns operation status, and error message | |
| try: | |
| card = ModelCard.load(repo_id) | |
| except huggingface_hub.utils.EntryNotFoundError: | |
| return False, "Please add a model card to your model to explain how you trained/fine-tuned it." | |
| # Enforce license metadata | |
| if card.data.license is None: | |
| if not ("license_name" in card.data and "license_link" in card.data): | |
| return False, ( | |
| "License not found. Please add a license to your model card using the `license` metadata or a" | |
| " `license_name`/`license_link` pair." | |
| ) | |
| # Enforce card content | |
| if len(card.text) < 200: | |
| return False, "Please add a description to your model card, it is too short." | |
| return True, "" | |
| def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str, AutoConfig]: | |
| try: | |
| config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) #, force_download=True) | |
| if test_tokenizer: | |
| try: | |
| tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) | |
| except ValueError as e: | |
| return ( | |
| False, | |
| f"uses a tokenizer which is not in a transformers release: {e}", | |
| None | |
| ) | |
| except Exception as e: | |
| return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None) | |
| return True, None, config | |
| except ValueError as e: | |
| return ( | |
| False, | |
| "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.", | |
| None | |
| ) | |
| except Exception as e: | |
| return False, "was not found on hub!", None | |
| def get_model_size(model_info: ModelInfo, precision: str): | |
| size_pattern = re.compile(r"(\d+\.)?\d+(b|m)") | |
| safetensors = None | |
| try: | |
| safetensors = get_safetensors_metadata(model_info.id) | |
| except Exception as e: | |
| print(e) | |
| if safetensors is not None: | |
| model_size = round(sum(safetensors.parameter_count.values()) / 1e9, 3) | |
| else: | |
| try: | |
| size_match = re.search(size_pattern, model_info.id.lower()) | |
| model_size = size_match.group(0) | |
| model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3) | |
| except AttributeError as e: | |
| return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py | |
| size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.id.lower()) else 1 | |
| model_size = size_factor * model_size | |
| return model_size | |
| def get_model_arch(model_info: ModelInfo): | |
| return model_info.config.get("architectures", "Unknown") | |
| def user_submission_permission(org_or_user, users_to_submission_dates, rate_limit_period, rate_limit_quota): | |
| if org_or_user not in users_to_submission_dates: | |
| return True, "" | |
| submission_dates = sorted(users_to_submission_dates[org_or_user]) | |
| time_limit = (datetime.now(timezone.utc) - timedelta(days=rate_limit_period)).strftime("%Y-%m-%dT%H:%M:%SZ") | |
| submissions_after_timelimit = [d for d in submission_dates if d > time_limit] | |
| num_models_submitted_in_period = len(submissions_after_timelimit) | |
| if org_or_user in HAS_HIGHER_RATE_LIMIT: | |
| rate_limit_quota = 2 * rate_limit_quota | |
| if num_models_submitted_in_period > rate_limit_quota: | |
| error_msg = f"Organisation or user `{org_or_user}`" | |
| error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard " | |
| error_msg += f"in the last {rate_limit_period} days.\n" | |
| error_msg += ( | |
| "Please wait a couple of days before resubmitting, so that everybody can enjoy using the leaderboard 🤗" | |
| ) | |
| return False, error_msg | |
| return True, "" | |
| def already_submitted_models(requested_models_dir: str) -> set[str]: | |
| depth = 1 | |
| file_names = [] | |
| users_to_submission_dates = defaultdict(list) | |
| for root, _, files in os.walk(requested_models_dir): | |
| current_depth = root.count(os.sep) - requested_models_dir.count(os.sep) | |
| if current_depth == depth: | |
| for file in files: | |
| if not file.endswith(".json"): | |
| continue | |
| with open(os.path.join(root, file), "r") as f: | |
| info = json.load(f) | |
| file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}") | |
| # Select organisation | |
| if info["model"].count("/") == 0 or "submitted_time" not in info: | |
| continue | |
| organisation, _ = info["model"].split("/") | |
| users_to_submission_dates[organisation].append(info["submitted_time"]) | |
| return set(file_names), users_to_submission_dates | |