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import json |
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import os |
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import re |
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from collections import defaultdict |
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from datetime import datetime, timedelta, timezone |
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import huggingface_hub |
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from huggingface_hub import ModelCard |
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from huggingface_hub.hf_api import ModelInfo |
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from transformers import AutoConfig |
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from transformers.models.auto.tokenization_auto import AutoTokenizer |
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def check_model_card(repo_id: str) -> tuple[bool, str]: |
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"""Checks if the model card and license exist and have been filled""" |
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try: |
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card = ModelCard.load(repo_id) |
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except huggingface_hub.utils.EntryNotFoundError: |
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return False, "Please add a model card to your model to explain how you trained/fine-tuned it." |
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if card.data.license is None: |
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if not ("license_name" in card.data and "license_link" in card.data): |
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return False, ( |
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"License not found. Please add a license to your model card using the `license` metadata or a" |
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" `license_name`/`license_link` pair." |
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) |
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if len(card.text) < 200: |
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return False, "Please add a description to your model card, it is too short." |
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return True, "" |
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def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]: |
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"""Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses.""" |
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try: |
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config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) |
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if test_tokenizer: |
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try: |
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tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) |
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except ValueError as e: |
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return ( |
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False, |
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f"uses a tokenizer which is not in a transformers release: {e}", |
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None |
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) |
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except Exception as e: |
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return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None) |
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return True, None, config |
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except ValueError: |
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return ( |
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False, |
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"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.", |
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None |
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) |
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except Exception as e: |
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return False, "was not found on hub!", None |
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def get_model_size(model_info: ModelInfo, precision: str): |
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"""Gets the model size from the configuration, or the model name if the configuration does not contain the information.""" |
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try: |
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model_size = round(model_info.safetensors["total"] / 1e9, 3) |
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except (AttributeError, TypeError): |
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return 0 |
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size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1 |
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model_size = size_factor * model_size |
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return model_size |
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def get_model_arch(model_info: ModelInfo): |
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"""Gets the model architecture from the configuration""" |
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return model_info.config.get("architectures", "Unknown") |
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def already_submitted_models(requested_models_dir: str) -> set[str]: |
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"""Gather a list of already submitted models to avoid duplicates""" |
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depth = 1 |
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file_names = [] |
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users_to_submission_dates = defaultdict(list) |
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for root, _, files in os.walk(requested_models_dir): |
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current_depth = root.count(os.sep) - requested_models_dir.count(os.sep) |
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if current_depth == depth: |
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for file in files: |
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if not file.endswith(".json"): |
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continue |
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with open(os.path.join(root, file), "r") as f: |
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info = json.load(f) |
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file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}") |
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if info["model"].count("/") == 0 or "submitted_time" not in info: |
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continue |
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organisation, _ = info["model"].split("/") |
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users_to_submission_dates[organisation].append(info["submitted_time"]) |
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return set(file_names), users_to_submission_dates |
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