alozowski's picture
alozowski HF staff
parms-count (#887)
e0f9194 verified
raw
history blame
7.58 kB
import json
import os
import re
import logging
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, get_safetensors_metadata
from transformers import AutoConfig, AutoTokenizer
from src.display.utils import parse_iso8601_datetime, curated_authors
from src.envs import HAS_HIGHER_RATE_LIMIT
# ht to @Wauplin, thank you for the snippet!
# See https://huggingface.co/spaces/open-llm-leaderboard/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.", None
# 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."
),
None,
)
# Enforce card content
if len(card.text) < 200:
return False, "Please add a description to your model card, it is too short.", None
return True, "", card
def is_model_on_hub(
model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False
) -> tuple[bool, str, AutoConfig]:
try:
config = AutoConfig.from_pretrained(
model_name, revision=revision, trust_remote_code=trust_remote_code, token=token, force_download=True)
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:
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:
if "You are trying to access a gated repo." in str(e):
return True, "uses a gated model.", None
return False, f"was not found or misconfigured on the hub! Error raised was {e.args[0]}", None
def get_model_size(model_info: ModelInfo, precision: str) -> float:
size_pattern = re.compile(r"(\d+\.)?\d+(b|m)")
safetensors = None
try:
safetensors = get_safetensors_metadata(model_info.id)
except Exception as e:
logging.error(f"Failed to get safetensors metadata for model {model_info.id}: {str(e)}")
if safetensors is not None:
model_size = round(sum(safetensors.parameter_count.values()) / 1e9, 3)
else:
try:
size_match = re.search(size_pattern, model_info.id.lower())
if size_match:
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)
else:
return -1 # Unknown model size
except AttributeError:
logging.warning(f"Unable to parse model size from ID: {model_info.id}")
return -1 # Unknown model size
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.id.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):
# No limit for curated authors
if org_or_user in curated_authors:
return True, ""
# Increase quota first if user has higher limits
if org_or_user in HAS_HIGHER_RATE_LIMIT:
rate_limit_quota *= 2
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)
submissions_after_timelimit = [
parse_iso8601_datetime(d) for d in submission_dates
if parse_iso8601_datetime(d) > time_limit
]
num_models_submitted_in_period = len(submissions_after_timelimit)
# Use >= to correctly enforce the rate limit
if num_models_submitted_in_period >= rate_limit_quota:
error_msg = f"Organisation or user `{org_or_user}` already has {num_models_submitted_in_period} model requests submitted 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']}")
# 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 get_model_tags(model_card, model: str):
is_merge_from_metadata = False
is_moe_from_metadata = False
tags = []
if model_card is None:
return tags
if model_card.data.tags:
is_merge_from_metadata = any(
[tag in model_card.data.tags for tag in ["merge", "moerge", "mergekit", "lazymergekit"]]
)
is_moe_from_metadata = any([tag in model_card.data.tags for tag in ["moe", "moerge"]])
is_merge_from_model_card = any(
keyword in model_card.text.lower() for keyword in ["merged model", "merge model", "moerge"]
)
if is_merge_from_model_card or is_merge_from_metadata:
tags.append("merge")
is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in ["moe", "mixtral"])
# Hardcoding because of gating problem
if "Qwen/Qwen1.5-32B" in model:
is_moe_from_model_card = False
is_moe_from_name = "moe" in model.lower().replace("/", "-").replace("_", "-").split("-")
if is_moe_from_model_card or is_moe_from_name or is_moe_from_metadata:
tags.append("moe")
return tags