''' 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() 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: 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__': 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()