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 from transformers import AutoConfig, AutoTokenizer from transformers.models.auto.tokenization_auto import tokenizer_class_from_name, get_tokenizer_config from src.envs import HAS_HIGHER_RATE_LIMIT from typing import Optional # 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, Optional[str], Optional[AutoConfig]]: try: config = AutoConfig.from_pretrained( model_name, revision=revision, trust_remote_code=trust_remote_code, token=token ) if test_tokenizer: try: 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, f"was not found on hub -- {str(e)}", None def get_model_size(model_info: ModelInfo, precision: str): size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)") try: model_size = round(model_info.safetensors["total"] / 1e9, 3) except (AttributeError, TypeError): try: size_match = re.search(size_pattern, model_info.modelId.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: 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.modelId.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']}_{info['inference_framework']}_{info['gpu_type']}") # 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