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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, get_safetensors_metadata
from transformers import AutoConfig, AutoTokenizer
# 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):
size_pattern = re.compile(r"(\d+\.)?\d+(b|m)")
safetensors = None
try:
safetensors = get_safetensors_metadata(model_info.id)
except Exception as e:
print(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())
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.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 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
def test():
model = "meta-llama/Meta-Llama-3-8B-Instruct"
# Test check_model_card
status, error, card = check_model_card(model)
# Test is_model_on_hub
status2, error2, config2 = is_model_on_hub(model, "main")
assert status == True
print(status2, error2, config2)
# Test get_model_size
model_info = ModelInfo(id=model)
precision = "GPTQ"
model_size = get_model_size(model_info, precision)
print(model_size)
import pdb
pdb.set_trace()
# Test get_model_arch
# model_arch = get_model_arch(model_info)
pass
if __name__ == "__main__":
test()
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