llama.cpp/gguf-py/scripts/hub-vocab.py

60 lines
1.6 KiB
Python

#!/usr/bin/env python3
from __future__ import annotations
import argparse
import logging
import os
import sys
from pathlib import Path
# Necessary to load the local gguf package
if (
"NO_LOCAL_GGUF" not in os.environ
and (Path(__file__).parent.parent.parent / "gguf-py").exists()
):
sys.path.insert(0, str(Path(__file__).parent.parent))
from gguf.constants import MODEL_ARCH, MODEL_ARCH_NAMES
from gguf.huggingface_hub import HFVocabRequest
logger = logging.getLogger(Path(__file__).stem)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("auth_token", help="A huggingface read auth token")
parser.add_argument(
"model_repo", help="A huggingface model repository, e.g. org/model"
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="Increase output verbosity."
)
parser.add_argument(
"-m",
"--model-path",
default="models/",
help="The models storage path. Default is 'models/'.",
)
parser.add_argument(
"--vocab-type",
const="BPE",
nargs="?",
choices=["SPM", "BPE", "WPM"],
help="The type of vocab. Default is 'BPE'.",
)
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
vocab_request = HFVocabRequest(args.auth_token, args.model_path, logger)
vocab_type = vocab_request.get_vocab_enum(args.vocab_type)
vocab_request.get_all_vocab_files(args.model_repo, vocab_type)
if __name__ == "__main__":
main()