| 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 |
| 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." |
|
|
| |
| 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." |
| ) |
|
|
| |
| 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]: |
| """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses.""" |
| try: |
| config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) |
| 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: |
| 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 |
|
|
| 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]: |
| """Gather a list of already submitted models to avoid duplicates""" |
| 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']}") |
|
|
| |
| 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 |
|
|
