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import pandas as pd
import re
from huggingface_hub import HfApi
api = HfApi()
def get_model_size(model_name, precision: str = "BF16", revision: str = "main"):
if len(model_name.split("/")) == 1:
return None
model_info = api.model_info(repo_id=model_name, revision=revision)
# model_size = get_model_size(model_info=model_info, precision=precision)
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
try:
model_size = round(model_info.safetensors["total"] / 1e9, 1)
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, 1)
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 make_clickable_model(model_name, link=None):
if len(model_name.split("/")) == 2:
link = "https://huggingface.co/" + model_name
return (
f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>'
)
return model_name
def load_data(data_path):
columns = ['Rank', 'Model', 'From', 'Open?', 'Params(B)', 'Cost', 'Score']
columns_sorted = ['Rank', 'Model', 'From', 'Open?', 'Params(B)', 'Cost', 'Score']
df = pd.read_csv(data_path, usecols=columns).dropna()
df['Score'] = df['Score'].round(0)
# rank according to the Score column
df = df.sort_values(by='Score', ascending=False)
# reorder the columns
df = df[columns_sorted]
# make the 'Model' column clickable
df['Model'] = df['Model'].apply(make_clickable_model)
return df
if __name__ == "__main__":
model_name = "SeaLLMs/SeaLLM-7B-v2"
model_size = get_model_size(model_name)
print(model_size) |