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import json
import os
import re
from collections import defaultdict
from datetime import datetime, timedelta, timezone
import logging
import huggingface_hub
from huggingface_hub import ModelCard, HfApi, hf_hub_download
from huggingface_hub.hf_api import ModelInfo
from transformers import AutoConfig
from transformers.models.auto.tokenization_auto import tokenizer_class_from_name, get_tokenizer_config
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO) # Only show INFO and above, hide DEBUG messages
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]:
"""Makes sure the model is on the hub, and uses a valid configuration (in the latest transformers version)"""
try:
config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
if test_tokenizer:
tokenizer_config = get_tokenizer_config(model_name)
if tokenizer_config is not None:
tokenizer_class_candidate = tokenizer_config.get("tokenizer_class", None)
else:
tokenizer_class_candidate = config.tokenizer_class
tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
if tokenizer_class is None:
return (
False,
f"uses {tokenizer_class_candidate}, which is not in a transformers release, therefore not supported at the moment.",
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]:
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']}")
# 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
def check_adapter_config_and_base_model(model_name: str, base_model: str, token: str = None) -> tuple[bool, str]:
"""
Checks if the model exists on HuggingFace and is accessible.
Args:
model_name: Name of the model to check
base_model: Expected base model name (not used anymore)
token: HuggingFace API token (optional)
Returns:
Tuple[bool, str]: A tuple containing:
- is_valid: Whether the model exists and is accessible
- error_message: Error message if the model is invalid
"""
try:
# Check if model exists on HuggingFace
# First try without token (for public models)
try:
# Try to access public model without token
api_public = HfApi()
model_info = api_public.model_info(repo_id=model_name)
logger.debug(f"Successfully accessed model {model_name}")
return True, None
except Exception as e:
logger.debug(f"Could not access model without token")
# If that fails, try with token (for private models)
if token:
try:
api_with_token = HfApi(token=token)
model_info = api_with_token.model_info(repo_id=model_name)
logger.debug(f"Successfully accessed model {model_name} with authentication")
return True, None
except Exception as e:
return False, f"Model {model_name} not found or not accessible: {str(e)}"
else:
return False, f"Model {model_name} not found or not accessible: {str(e)}"
except Exception as e:
return False, f"Error validating model: {str(e)}"
def has_adapter_config(model_name: str, token: str = None) -> tuple[bool, str]:
"""
Checks if the model repository contains adapter configuration files.
Args:
model_name: Name of the model to check
token: HuggingFace API token (optional)
Returns:
Tuple[bool, str]: A tuple containing:
- has_adapter: Whether the model contains adapter configuration
- message: Additional information or error message
"""
try:
# Initialize API with or without token
api = HfApi(token=token) if token else HfApi()
# Get the list of files in the repository
repo_files = api.list_repo_files(repo_id=model_name)
# Check for specific adapter configuration files
adapter_files = [
"adapter_config.json",
"adapter_model.bin",
"adapter_model.safetensors",
"adapter.json",
"adapter.safetensors",
"adapter.bin"
]
# Look for specific adapter files
found_adapter_files = []
for file in repo_files:
file_lower = file.lower()
if any(adapter_file.lower() in file_lower for adapter_file in adapter_files):
found_adapter_files.append(file)
# Check if we found adapter configuration
has_adapter = len(found_adapter_files) > 0
if has_adapter:
adapter_files_str = ", ".join(found_adapter_files)
return True, f"Found adapter configuration: {adapter_files_str}"
else:
return False, "No adapter configuration found"
except Exception as e:
return False, f"Error checking for adapter configuration: {str(e)}"
def has_safetensor_model(model_name: str, token: str = None) -> tuple[bool, str]:
"""
Checks if the model repository contains safetensor model files.
Args:
model_name: Name of the model to check
token: HuggingFace API token (optional)
Returns:
Tuple[bool, str]: A tuple containing:
- has_safetensor: Whether the model contains safetensor model files
- message: Additional information or error message
"""
try:
# Initialize API with or without token
api = HfApi(token=token) if token else HfApi()
# Get the list of files in the repository
repo_files = api.list_repo_files(repo_id=model_name)
# Look for safetensor model files (model_*.safetensors)
safetensor_files = []
model_pattern = "model_"
safetensor_extension = ".safetensors"
for file in repo_files:
file_lower = file.lower()
if model_pattern in file_lower and file_lower.endswith(safetensor_extension):
safetensor_files.append(file)
# Check if we found any safetensor model files
has_safetensor = len(safetensor_files) > 0
if has_safetensor:
safetensor_files_str = ", ".join(safetensor_files)
return True, f"Found safetensor model files: {safetensor_files_str}"
else:
# If no model_*.safetensors files, check for any .safetensors files
any_safetensor_files = [file for file in repo_files if file.lower().endswith(safetensor_extension)]
if any_safetensor_files:
safetensor_files_str = ", ".join(any_safetensor_files)
return True, f"Found safetensor files: {safetensor_files_str}"
else:
return False, "No safetensor model files found"
except Exception as e:
return False, f"Error checking for safetensor model files: {str(e)}"
def determine_model_type(model_name: str, token: str = None) -> tuple[str, str]:
"""
Determines the type of model based on the files in the repository.
Args:
model_name: Name of the model to check
token: HuggingFace API token (optional)
Returns:
Tuple[str, str]: A tuple containing:
- model_type: Type of model (adapter, merged_model, unknown)
- message: Additional information or details
"""
try:
# Check for adapter configuration
has_adapter, adapter_message = has_adapter_config(model_name, token)
# Check for safetensor model files
has_safetensor, safetensor_message = has_safetensor_model(model_name, token)
# Determine model type based on checks
if has_adapter:
return "adapter", adapter_message
elif has_safetensor:
return "merged_model", safetensor_message
else:
return "unknown", "Could not determine model type: no adapter config or safetensor model files found"
except Exception as e:
return "unknown", f"Error determining model type: {str(e)}"
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