mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-29 19:09:32 +01:00
201 lines
7.2 KiB
Python
201 lines
7.2 KiB
Python
'''
|
|
Downloads models from Hugging Face to models/model-name.
|
|
|
|
Example:
|
|
python download-model.py facebook/opt-1.3b
|
|
|
|
'''
|
|
|
|
import argparse
|
|
import base64
|
|
import datetime
|
|
import json
|
|
import re
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import requests
|
|
import tqdm
|
|
from tqdm.contrib.concurrent import thread_map
|
|
|
|
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.')
|
|
args = parser.parse_args()
|
|
|
|
def get_file(url, output_folder):
|
|
r = requests.get(url, stream=True)
|
|
with open(output_folder / Path(url.rsplit('/', 1)[1]), 'wb') as f:
|
|
total_size = int(r.headers.get('content-length', 0))
|
|
block_size = 1024
|
|
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:
|
|
for data in r.iter_content(block_size):
|
|
t.update(len(data))
|
|
f.write(data)
|
|
|
|
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].strip().upper()
|
|
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
|
|
|
|
def get_download_links_from_huggingface(model, branch):
|
|
base = "https://huggingface.co"
|
|
page = f"/api/models/{model}/tree/{branch}?cursor="
|
|
cursor = b""
|
|
|
|
links = []
|
|
sha256 = []
|
|
classifications = []
|
|
has_pytorch = False
|
|
has_pt = False
|
|
has_safetensors = False
|
|
is_lora = False
|
|
while True:
|
|
content = requests.get(f"{base}{page}{cursor.decode()}").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_tokenizer = re.match("tokenizer.*\.model", fname)
|
|
is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
|
|
|
|
if any((is_pytorch, is_safetensors, is_pt, 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 args.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')
|
|
|
|
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 download_files(file_list, output_folder, num_threads=8):
|
|
thread_map(lambda url: get_file(url, output_folder), file_list, max_workers=num_threads)
|
|
|
|
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()
|
|
|
|
links, sha256, is_lora = get_download_links_from_huggingface(model, branch)
|
|
|
|
if args.output is not None:
|
|
base_folder = args.output
|
|
else:
|
|
base_folder = 'models' if not is_lora else 'loras'
|
|
|
|
output_folder = f"{'_'.join(model.split('/')[-2:])}"
|
|
if branch != 'main':
|
|
output_folder += f'_{branch}'
|
|
|
|
# Creating the folder and writing the metadata
|
|
output_folder = Path(base_folder) / output_folder
|
|
if not output_folder.exists():
|
|
output_folder.mkdir()
|
|
with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
|
|
f.write(f'url: https://huggingface.co/{model}\n')
|
|
f.write(f'branch: {branch}\n')
|
|
f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
|
|
sha256_str = ''
|
|
for i in range(len(sha256)):
|
|
sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
|
|
if sha256_str != '':
|
|
f.write(f'sha256sum:\n{sha256_str}')
|
|
|
|
# Downloading the files
|
|
print(f"Downloading the model to {output_folder}")
|
|
download_files(links, output_folder, args.threads)
|
|
print()
|