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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, AutoTokenizer
from transformers.models.auto.tokenization_auto import tokenizer_class_from_name, get_tokenizer_config
from src.envs import HAS_HIGHER_RATE_LIMIT
from typing import Optional
# ht to @Wauplin, thank you for the snippet!
# See https://huggingface.co/spaces/HuggingFaceH4/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."
# 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, Optional[str], Optional[AutoConfig]]:
try:
config = AutoConfig.from_pretrained(
model_name, revision=revision, trust_remote_code=trust_remote_code, token=token
)
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 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 as e:
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, f"was not found on hub -- {str(e)}", None
def get_model_size(model_info: ModelInfo, precision: str):
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
try:
model_size = round(model_info.safetensors["total"] / 1e9, 3)
except (AttributeError, TypeError):
try:
size_match = re.search(size_pattern, model_info.modelId.lower())
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)
except AttributeError:
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):
return model_info.config.get("architectures", "Unknown")
def user_submission_permission(org_or_user, users_to_submission_dates, rate_limit_period, rate_limit_quota):
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)).strftime("%Y-%m-%dT%H:%M:%SZ")
submissions_after_timelimit = [d for d in submission_dates if d > time_limit]
num_models_submitted_in_period = len(submissions_after_timelimit)
if org_or_user in HAS_HIGHER_RATE_LIMIT:
rate_limit_quota = 2 * rate_limit_quota
if num_models_submitted_in_period > rate_limit_quota:
error_msg = f"Organisation or user `{org_or_user}`"
error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
error_msg += f"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']}_{info['inference_framework']}")
# 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
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