text-generation-webui/download-model.py

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'''
Downloads models from Hugging Face to models/model-name.
Example:
python download-model.py facebook/opt-1.3b
'''
import requests
from bs4 import BeautifulSoup
import multiprocessing
import tqdm
import sys
import argparse
from pathlib import Path
import re
parser = argparse.ArgumentParser()
parser.add_argument('MODEL', type=str)
parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
args = parser.parse_args()
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def get_file(args):
url = args[0]
output_folder = args[1]
r = requests.get(url, stream=True)
with open(output_folder / Path(url.split('/')[-1]), 'wb') as f:
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total_size = int(r.headers.get('content-length', 0))
block_size = 1024
t = tqdm.tqdm(total=total_size, unit='iB', unit_scale=True)
for data in r.iter_content(block_size):
t.update(len(data))
f.write(data)
t.close()
def sanitize_branch_name(branch_name):
pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
if pattern.match(branch_name):
return branch_name
else:
raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
if __name__ == '__main__':
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model = args.MODEL
if model[-1] == '/':
model = model[:-1]
branch = args.branch
if args.branch is None:
branch = 'main'
else:
try:
branch_name = args.branch
branch = sanitize_branch_name(branch_name)
except ValueError as err_branch:
print(f"Error: {err_branch}")
sys.exit()
url = f'https://huggingface.co/{model}/tree/{branch}'
if branch != 'main':
output_folder = Path("models") / (model.split('/')[-1] + f'_{branch}')
else:
output_folder = Path("models") / model.split('/')[-1]
if not output_folder.exists():
output_folder.mkdir()
# Finding the relevant files to download
page = requests.get(url)
soup = BeautifulSoup(page.content, 'html.parser')
links = soup.find_all('a')
downloads = []
for link in links:
href = link.get('href')[1:]
if href.startswith(f'{model}/resolve/{branch}'):
if href.endswith(('.json', '.txt')) or (href.endswith('.bin') and 'pytorch_model' in href):
downloads.append(f'https://huggingface.co/{href}')
# Downloading the files
print(f"Downloading the model to {output_folder}...")
pool = multiprocessing.Pool(processes=4)
results = pool.map(get_file, [[downloads[i], output_folder] for i in range(len(downloads))])
pool.close()
pool.join()