import json import os import re import logging 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, parse_safetensors_file_metadata from transformers import AutoConfig, AutoTokenizer from src.display.utils import parse_iso8601_datetime, curated_authors from src.envs import HAS_HIGHER_RATE_LIMIT # ht to @Wauplin, thank you for the snippet! # See https://huggingface.co/spaces/open-llm-leaderboard/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.", None # Enforce license metadata if card.data.license is None and 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." ), None, ) # Enforce card content if len(card.text) < 200: return False, "Please add a description to your model card, it is too short.", None return True, "", card def is_model_on_hub( model_name: str, revision: str, token: str | None = None, trust_remote_code: bool = False, test_tokenizer: bool = 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: 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: return ( False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None, ) except Exception: 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: 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: if "You are trying to access a gated repo." in str(e): return True, "uses a gated model.", None return False, f"was not found or misconfigured on the hub! Error raised was {e.args[0]}", None def get_model_size(model_info: ModelInfo, precision: str, base_model: str| None) -> tuple[float | None, str]: size_pattern = re.compile(r"(\d+\.)?\d+(b|m)") safetensors = None adapter_safetensors = None # hack way to check that model is adapter is_adapter = "adapter_config.json" in (s.rfilename for s in model_info.siblings) try: if is_adapter: if not base_model: return None, "Adapter model submission detected. Please ensure the base model information is provided." adapter_safetensors = parse_safetensors_file_metadata(model_info.id, "adapter_model.safetensors") safetensors = get_safetensors_metadata(base_model) else: safetensors = get_safetensors_metadata(model_info.id) except Exception as e: logging.warning(f"Failed to get safetensors metadata for model {model_info.id}: {e!s}") if safetensors is not None: model_size = sum(safetensors.parameter_count.values()) if adapter_safetensors is not None: model_size += sum(safetensors.parameter_count.values()) model_size = round(model_size / 1e9, 3) else: try: size_match = re.search(size_pattern, model_info.id.lower()) if size_match: 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) else: return None, "Unknown model size" except AttributeError: logging.warning(f"Unable to parse model size from ID: {model_info.id}") return None, "Unknown model size" 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): # No limit for curated authors if org_or_user in curated_authors: return True, "" # Increase quota first if user has higher limits if org_or_user in HAS_HIGHER_RATE_LIMIT: rate_limit_quota *= 2 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) submissions_after_timelimit = [ parse_iso8601_datetime(d) for d in submission_dates if parse_iso8601_datetime(d) > time_limit ] num_models_submitted_in_period = len(submissions_after_timelimit) # Use >= to correctly enforce the rate limit if num_models_submitted_in_period >= rate_limit_quota: error_msg = f"Organisation or user `{org_or_user}` already has {num_models_submitted_in_period} model requests submitted 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 def get_model_tags(model_card, model: str): is_merge_from_metadata = False is_moe_from_metadata = False tags = [] if model_card is None: return tags if model_card.data.tags: is_merge_from_metadata = any( [tag in model_card.data.tags for tag in ["merge", "moerge", "mergekit", "lazymergekit"]] ) is_moe_from_metadata = any([tag in model_card.data.tags for tag in ["moe", "moerge"]]) is_merge_from_model_card = any( keyword in model_card.text.lower() for keyword in ["merged model", "merge model", "moerge"] ) if is_merge_from_model_card or is_merge_from_metadata: tags.append("merge") is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in ["moe", "mixtral"]) # Hardcoding because of gating problem if "Qwen/Qwen1.5-32B" in model: is_moe_from_model_card = False is_moe_from_name = "moe" in model.lower().replace("/", "-").replace("_", "-").split("-") if is_moe_from_model_card or is_moe_from_name or is_moe_from_metadata: tags.append("moe") return tags