File size: 5,669 Bytes
699e8ff 8c49cb6 0799cf8 460d762 8c49cb6 6254b87 460d762 8c49cb6 460d762 1df8383 460d762 0799cf8 6254b87 460d762 0799cf8 460d762 0799cf8 460d762 8c49cb6 460d762 8c49cb6 12cea14 460d762 8c49cb6 460d762 8c49cb6 460d762 1df8383 12cea14 1df8383 460d762 699e8ff 8c49cb6 699e8ff 8c49cb6 699e8ff 8c49cb6 699e8ff 8c49cb6 9e0f1e6 699e8ff 8c49cb6 699e8ff 8c49cb6 699e8ff 8c49cb6 699e8ff ed1fdef 8c49cb6 460d762 ed1fdef d52179b 460d762 699e8ff |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import glob
import json
import os
import re
import pickle
from typing import List
import huggingface_hub
from huggingface_hub import HfApi
from tqdm import tqdm
from src.display_models.model_metadata_flags import DO_NOT_SUBMIT_MODELS, FLAGGED_MODELS
from src.display_models.model_metadata_type import MODEL_TYPE_METADATA, ModelType, model_type_from_str
from src.display_models.utils import AutoEvalColumn, model_hyperlink
api = HfApi(token=os.environ.get("H4_TOKEN", None))
def get_model_infos_from_hub(leaderboard_data: List[dict]):
# load cache from disk
try:
with open("model_info_cache.pkl", "rb") as f:
model_info_cache = pickle.load(f)
except EOFError:
model_info_cache = {}
for model_data in tqdm(leaderboard_data):
model_name = model_data["model_name_for_query"]
if model_name in model_info_cache:
model_info = model_info_cache[model_name]
else:
try:
model_info = api.model_info(model_name)
model_info_cache[model_name] = model_info
except huggingface_hub.utils._errors.RepositoryNotFoundError:
print("Repo not found!", model_name)
model_data[AutoEvalColumn.license.name] = None
model_data[AutoEvalColumn.likes.name] = None
model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
continue
model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info)
model_data[AutoEvalColumn.params.name] = get_model_size(model_name, model_info)
# save cache to disk in pickle format
with open("model_info_cache.pkl", "wb") as f:
pickle.dump(model_info_cache, f)
def get_model_license(model_info):
try:
return model_info.cardData["license"]
except Exception:
return None
def get_model_likes(model_info):
return model_info.likes
size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
def get_model_size(model_name, model_info):
# In billions
try:
return round(model_info.safetensors["total"] / 1e9, 3)
except AttributeError:
try:
size_match = re.search(size_pattern, model_name.lower())
size = size_match.group(0)
return round(float(size[:-1]) if size[-1] == "b" else float(size[:-1]) / 1e3, 3)
except AttributeError:
return None
def get_model_type(leaderboard_data: List[dict]):
for model_data in leaderboard_data:
request_files = os.path.join(
"eval-queue",
model_data["model_name_for_query"] + "_eval_request_*" + ".json",
)
request_files = glob.glob(request_files)
# Select correct request file (precision)
request_file = ""
if len(request_files) == 1:
request_file = request_files[0]
elif len(request_files) > 1:
request_files = sorted(request_files, reverse=True)
for tmp_request_file in request_files:
with open(tmp_request_file, "r") as f:
req_content = json.load(f)
if (
req_content["status"] == "FINISHED"
and req_content["precision"] == model_data["Precision"].split(".")[-1]
):
request_file = tmp_request_file
try:
with open(request_file, "r") as f:
request = json.load(f)
model_type = model_type_from_str(request["model_type"])
model_data[AutoEvalColumn.model_type.name] = model_type.value.name
model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol # + ("🔺" if is_delta else "")
except Exception:
if model_data["model_name_for_query"] in MODEL_TYPE_METADATA:
model_data[AutoEvalColumn.model_type.name] = MODEL_TYPE_METADATA[
model_data["model_name_for_query"]
].value.name
model_data[AutoEvalColumn.model_type_symbol.name] = MODEL_TYPE_METADATA[
model_data["model_name_for_query"]
].value.symbol # + ("🔺" if is_delta else "")
else:
model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name
model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol
def flag_models(leaderboard_data: List[dict]):
for model_data in leaderboard_data:
if model_data["model_name_for_query"] in FLAGGED_MODELS:
issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1]
issue_link = model_hyperlink(
FLAGGED_MODELS[model_data["model_name_for_query"]],
f"See discussion #{issue_num}",
)
model_data[
AutoEvalColumn.model.name
] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
def remove_forbidden_models(leaderboard_data: List[dict]):
indices_to_remove = []
for ix, model in enumerate(leaderboard_data):
if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
indices_to_remove.append(ix)
for ix in reversed(indices_to_remove):
leaderboard_data.pop(ix)
return leaderboard_data
def apply_metadata(leaderboard_data: List[dict]):
leaderboard_data = remove_forbidden_models(leaderboard_data)
get_model_type(leaderboard_data)
get_model_infos_from_hub(leaderboard_data)
flag_models(leaderboard_data)
|