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import json
import os
from typing import Any
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, model_args: dict = None, token: str = None, test_tokenizer=False) -> tuple[bool, str, Any]:
"""Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
model_args = model_args or {}
try:
config = AutoConfig.from_pretrained(model_name, token=token, **model_args)
if test_tokenizer:
try:
tk = AutoTokenizer.from_pretrained(model_name, token=token, **model_args)
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 = model_info.safetensors["total"]
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 get_model_properties(configuration: dict) -> tuple[str, str, str, int, str, int]:
model_name = configuration["model_name_sanitized"]
revision = configuration["config"]["model_revision"]
precision = configuration["config"]["model_dtype"].split(".")[-1]
seed = configuration["config"]["random_seed"]
n_shot = list(configuration["n-shot"].values())[0]
prompt_version = list(configuration["versions"].values())[0]
return model_name, revision, precision, seed, prompt_version, n_shot
def already_submitted_models(requested_models_dir: str) -> set[str]:
"""Gather a list of already submitted models to avoid duplicates"""
depth = 1
run_names = []
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)
properties = get_model_properties(info)
run_names.append("_".join([str(property) for property in properties]))
return set(run_names)
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