forked from third-party-mirrors/ollama
148 lines
4.6 KiB
Python
148 lines
4.6 KiB
Python
import requests
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import validators
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from pathlib import Path
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from os import path, walk
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from urllib.parse import urlsplit, urlunsplit
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from tqdm import tqdm
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MODELS_MANIFEST = 'https://ollama.ai/api/models'
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MODELS_CACHE_PATH = Path.home() / '.ollama' / 'models'
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def models(*args, **kwargs):
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for _, _, files in walk(MODELS_CACHE_PATH):
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for file in files:
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base, ext = path.splitext(file)
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if ext == '.bin':
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yield base
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# search the directory and return all models which contain the search term as a substring,
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# or all models if no search term is provided
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def search_directory(query):
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response = requests.get(MODELS_MANIFEST)
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response.raise_for_status()
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directory = response.json()
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model_names = []
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for model_info in directory:
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if not query or query.lower() in model_info.get('name', '').lower():
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model_names.append(model_info.get('name'))
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return model_names
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# get the url of the model from our curated directory
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def get_url_from_directory(model):
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response = requests.get(MODELS_MANIFEST)
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response.raise_for_status()
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directory = response.json()
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for model_info in directory:
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if model_info.get('name').lower() == model.lower():
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return model_info.get('url')
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return model
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def download_from_repo(url, file_name):
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parts = urlsplit(url)
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path_parts = parts.path.split('/tree/')
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if len(path_parts) == 1:
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location = path_parts[0]
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branch = 'main'
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else:
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location, branch = path_parts
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location = location.strip('/')
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if file_name == '':
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file_name = path.basename(location).lower()
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download_url = urlunsplit(
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(
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'https',
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parts.netloc,
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f'/api/models/{location}/tree/{branch}',
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parts.query,
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parts.fragment,
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)
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)
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response = requests.get(download_url)
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response.raise_for_status()
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json_response = response.json()
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download_url, file_size = find_bin_file(json_response, location, branch)
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return download_file(download_url, file_name, file_size)
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def find_bin_file(json_response, location, branch):
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download_url = None
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file_size = 0
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for file_info in json_response:
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if file_info.get('type') == 'file' and file_info.get('path').endswith('.bin'):
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f_path = file_info.get('path')
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download_url = (
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f'https://huggingface.co/{location}/resolve/{branch}/{f_path}'
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)
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file_size = file_info.get('size')
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if download_url is None:
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raise Exception('No model found')
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return download_url, file_size
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def download_file(download_url, file_name, file_size):
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local_filename = MODELS_CACHE_PATH / str(file_name + '.bin')
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first_byte = path.getsize(local_filename) if path.exists(local_filename) else 0
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if first_byte >= file_size:
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return local_filename
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print(f'Pulling {file_name}...')
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header = {'Range': f'bytes={first_byte}-'} if first_byte != 0 else {}
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response = requests.get(download_url, headers=header, stream=True)
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response.raise_for_status()
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total_size = int(response.headers.get('content-length', 0)) + first_byte
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with open(local_filename, 'ab' if first_byte else 'wb') as file, tqdm(
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total=total_size,
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unit='iB',
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unit_scale=True,
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unit_divisor=1024,
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initial=first_byte,
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ascii=' ==',
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bar_format='Downloading [{bar}] {percentage:3.2f}% {rate_fmt}{postfix}',
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) as bar:
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for data in response.iter_content(chunk_size=1024):
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size = file.write(data)
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bar.update(size)
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return local_filename
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def pull(model_name, *args, **kwargs):
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maybe_existing_model_location = MODELS_CACHE_PATH / str(model_name + '.bin')
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if path.exists(model_name) or path.exists(maybe_existing_model_location):
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# a file on the filesystem is being specified
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return model_name
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# check the remote model location and see if it needs to be downloaded
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url = model_name
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file_name = ""
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if not validators.url(url) and not url.startswith('huggingface.co'):
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url = get_url_from_directory(model_name)
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file_name = model_name
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if not (url.startswith('http://') or url.startswith('https://')):
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url = f'https://{url}'
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if not validators.url(url):
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if model_name in models(MODELS_CACHE_PATH):
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# the model is already downloaded, and specified by name
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return model_name
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raise Exception(f'Unknown model {model_name}')
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local_filename = download_from_repo(url, file_name)
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return local_filename
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