text-generation-webui / download-model.py
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'''
Downloads models from Hugging Face to models/username_modelname.
Example:
python download-model.py facebook/opt-1.3b
'''
import argparse
import base64
import datetime
import hashlib
import json
import os
import re
import sys
from pathlib import Path
import requests
import tqdm
from tqdm.contrib.concurrent import thread_map
class ModelDownloader:
def __init__(self):
self.s = requests.Session()
if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
def sanitize_model_and_branch_names(self, model, branch):
if model[-1] == '/':
model = model[:-1]
if branch is None:
branch = "main"
else:
pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
if not pattern.match(branch):
raise ValueError(
"Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")
return model, branch
def get_download_links_from_huggingface(self, model, branch, text_only=False):
base = "https://huggingface.co"
page = f"/api/models/{model}/tree/{branch}"
cursor = b""
links = []
sha256 = []
classifications = []
has_pytorch = False
has_pt = False
# has_ggml = False
has_safetensors = False
is_lora = False
while True:
url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
r = self.s.get(url, timeout=20)
r.raise_for_status()
content = r.content
dict = json.loads(content)
if len(dict) == 0:
break
for i in range(len(dict)):
fname = dict[i]['path']
if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
is_lora = True
is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname)
is_safetensors = re.match(".*\.safetensors", fname)
is_pt = re.match(".*\.pt", fname)
is_ggml = re.match(".*ggml.*\.bin", fname)
is_tokenizer = re.match("(tokenizer|ice).*\.model", fname)
is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)):
if 'lfs' in dict[i]:
sha256.append([fname, dict[i]['lfs']['oid']])
if is_text:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
classifications.append('text')
continue
if not text_only:
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
if is_safetensors:
has_safetensors = True
classifications.append('safetensors')
elif is_pytorch:
has_pytorch = True
classifications.append('pytorch')
elif is_pt:
has_pt = True
classifications.append('pt')
elif is_ggml:
# has_ggml = True
classifications.append('ggml')
cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
cursor = base64.b64encode(cursor)
cursor = cursor.replace(b'=', b'%3D')
# If both pytorch and safetensors are available, download safetensors only
if (has_pytorch or has_pt) and has_safetensors:
for i in range(len(classifications) - 1, -1, -1):
if classifications[i] in ['pytorch', 'pt']:
links.pop(i)
return links, sha256, is_lora
def get_output_folder(self, model, branch, is_lora, base_folder=None):
if base_folder is None:
base_folder = 'models' if not is_lora else 'loras'
output_folder = f"{'_'.join(model.split('/')[-2:])}"
if branch != 'main':
output_folder += f'_{branch}'
output_folder = Path(base_folder) / output_folder
return output_folder
def get_single_file(self, url, output_folder, start_from_scratch=False):
filename = Path(url.rsplit('/', 1)[1])
output_path = output_folder / filename
headers = {}
mode = 'wb'
if output_path.exists() and not start_from_scratch:
# Check if the file has already been downloaded completely
r = self.s.get(url, stream=True, timeout=20)
total_size = int(r.headers.get('content-length', 0))
if output_path.stat().st_size >= total_size:
return
# Otherwise, resume the download from where it left off
headers = {'Range': f'bytes={output_path.stat().st_size}-'}
mode = 'ab'
with self.s.get(url, stream=True, headers=headers, timeout=20) as r:
r.raise_for_status() # Do not continue the download if the request was unsuccessful
total_size = int(r.headers.get('content-length', 0))
block_size = 1024 * 1024 # 1MB
with open(output_path, mode) as f:
with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
count = 0
for data in r.iter_content(block_size):
t.update(len(data))
f.write(data)
if total_size != 0 and self.progress_bar is not None:
count += len(data)
self.progress_bar(float(count) / float(total_size), f"Downloading {filename}")
def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1):
self.progress_bar = progress_bar
# Creating the folder and writing the metadata
output_folder.mkdir(parents=True, exist_ok=True)
metadata = f'url: https://huggingface.co/{model}\n' \
f'branch: {branch}\n' \
f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'
sha256_str = '\n'.join([f' {item[1]} {item[0]}' for item in sha256])
if sha256_str:
metadata += f'sha256sum:\n{sha256_str}'
metadata += '\n'
(output_folder / 'huggingface-metadata.txt').write_text(metadata)
# Downloading the files
print(f"Downloading the model to {output_folder}")
self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
def check_model_files(self, model, branch, links, sha256, output_folder):
# Validate the checksums
validated = True
for i in range(len(sha256)):
fpath = (output_folder / sha256[i][0])
if not fpath.exists():
print(f"The following file is missing: {fpath}")
validated = False
continue
with open(output_folder / sha256[i][0], "rb") as f:
bytes = f.read()
file_hash = hashlib.sha256(bytes).hexdigest()
if file_hash != sha256[i][1]:
print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}')
validated = False
else:
print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}')
if validated:
print('[+] Validated checksums of all model files!')
else:
print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')
if __name__ == '__main__':
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).')
parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
args = parser.parse_args()
branch = args.branch
model = args.MODEL
if model is None:
print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
sys.exit()
downloader = ModelDownloader()
# Cleaning up the model/branch names
try:
model, branch = downloader.sanitize_model_and_branch_names(model, branch)
except ValueError as err_branch:
print(f"Error: {err_branch}")
sys.exit()
# Getting the download links from Hugging Face
links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only)
# Getting the output folder
output_folder = downloader.get_output_folder(model, branch, is_lora, base_folder=args.output)
if args.check:
# Check previously downloaded files
downloader.check_model_files(model, branch, links, sha256, output_folder)
else:
# Download files
downloader.download_model_files(model, branch, links, sha256, output_folder, threads=args.threads)