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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." | |
# 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]: | |
"""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 # 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]: | |
"""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['precision']}_{info['add_special_tokens']}") | |
# 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 | |