|
|
|
|
|
|
|
|
|
import json |
|
import os |
|
from collections import defaultdict |
|
|
|
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." |
|
|
|
|
|
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." |
|
) |
|
|
|
|
|
|
|
|
|
|
|
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: |
|
_ = 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: |
|
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: |
|
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 |
|
|
|
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['revision']}_{info['precision']}") |
|
|
|
|
|
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 |
|
|