import json import os from collections import defaultdict import huggingface_hub from huggingface_hub import ModelCard from huggingface_hub.hf_api import ModelInfo from transformers import AutoConfig from transformers.models.auto.tokenization_auto import AutoTokenizer def check_model_card(repo_id: str) -> tuple[bool, str]: """Checks if the model card and license exist and have been filled""" 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]: try: config = AutoConfig.from_pretrained(model_name, revision=revision, token=token, trust_remote_code=trust_remote_code) if test_tokenizer: try: print("TOKEN: %s" % token) print("model name: %s" % model_name) print("revision: %s" % revision) print("trust remote code: %s" % trust_remote_code) _ = AutoTokenizer.from_pretrained(model_name, revision=revision, token=token, trust_remote_code=trust_remote_code) 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, f"'s tokenizer cannot be loaded. This most commonly happens when your tokenizer class is not in a stable transformers release or not correctly configured?\nExact error is: {e}", 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: return False, "was not found on hub!", None def get_model_size(model_info: ModelInfo, precision: str): """Gets the model size from the configuration, or the model name if the configuration does not contain the information.""" try: model_size = round(model_info.safetensors["total"] / 1e9, 3) except (AttributeError, TypeError): 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): """Gets the model architecture from the configuration""" return model_info.config.get("architectures", "Unknown") 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