Spaces:
Runtime error
Runtime error
File size: 5,671 Bytes
308f73c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
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
# 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, str]:
# try:
# config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
# 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 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):
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']}")
# 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
|