text-generation-webui/download-model.py

160 lines
5.5 KiB
Python

'''
Downloads models from Hugging Face to models/model-name.
Example:
python download-model.py facebook/opt-1.3b
'''
import argparse
import multiprocessing
import re
import sys
from pathlib import Path
import requests
import tqdm
from bs4 import BeautifulSoup
parser = argparse.ArgumentParser()
parser.add_argument('MODEL', type=str, default=None, nargs='?')
parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
args = parser.parse_args()
def get_file(args):
url = args[0]
output_folder = args[1]
idx = args[2]
tot = args[3]
print(f"Downloading file {idx} of {tot}...")
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.")
def select_model_from_default_options():
models = {
"Pygmalion 6B original": ("PygmalionAI", "pygmalion-6b", "b8344bb4eb76a437797ad3b19420a13922aaabe1"),
"Pygmalion 6B main": ("PygmalionAI", "pygmalion-6b", "main"),
"Pygmalion 6B dev": ("PygmalionAI", "pygmalion-6b", "dev"),
"Pygmalion 2.7B": ("PygmalionAI", "pygmalion-2.7b", "main"),
"Pygmalion 1.3B": ("PygmalionAI", "pygmalion-1.3b", "main"),
"Pygmalion 350m": ("PygmalionAI", "pygmalion-350m", "main"),
"OPT 6.7b": ("facebook", "opt-6.7b", "main"),
"OPT 2.7b": ("facebook", "opt-2.7b", "main"),
"OPT 1.3b": ("facebook", "opt-1.3b", "main"),
"OPT 350m": ("facebook", "opt-350m", "main"),
}
choices = {}
print("Select the model that you want to download:\n")
for i,name in enumerate(models):
char = chr(ord('A')+i)
choices[char] = name
print(f"{char}) {name}")
char = chr(ord('A')+len(models))
print(f"{char}) None of the above")
print()
print("Input> ", end='')
choice = input()[0]
if choice == char:
print("""\nThen type the name of your desired Hugging Face model in the format organization/name.
Examples:
PygmalionAI/pygmalion-6b
facebook/opt-1.3b
""")
print("Input> ", end='')
model = input()
branch = "main"
else:
arr = models[choices[choice]]
model = f"{arr[0]}/{arr[1]}"
branch = arr[2]
return model, branch
if __name__ == '__main__':
model = args.MODEL
branch = args.branch
if model is None:
model, branch = select_model_from_default_options()
else:
if model[-1] == '/':
model = model[:-1]
branch = args.branch
if branch is None:
branch = "main"
else:
try:
branch = sanitize_branch_name(branch)
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 = []
classifications = []
has_pytorch = False
has_safetensors = False
for link in links:
href = link.get('href')[1:]
if href.startswith(f'{model}/resolve/{branch}'):
fname = Path(href).name
is_pytorch = re.match("pytorch_model.*\.bin", fname)
is_safetensors = re.match("model.*\.safetensors", fname)
is_text = re.match(".*\.(txt|json)", fname)
if is_text or is_safetensors or is_pytorch:
if is_text:
downloads.append(f'https://huggingface.co/{href}')
classifications.append('text')
continue
if not args.text_only:
downloads.append(f'https://huggingface.co/{href}')
if is_safetensors:
has_safetensors = True
classifications.append('safetensors')
elif is_pytorch:
has_pytorch = True
classifications.append('pytorch')
# If both pytorch and safetensors are available, download safetensors only
if has_pytorch and has_safetensors:
for i in range(len(classifications)-1, -1, -1):
if classifications[i] == 'pytorch':
downloads.pop(i)
# Downloading the files
print(f"Downloading the model to {output_folder}")
pool = multiprocessing.Pool(processes=args.threads)
results = pool.map(get_file, [[downloads[i], output_folder, i+1, len(downloads)] for i in range(len(downloads))])
pool.close()
pool.join()