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#!/usr/bin/env python3
import os
import argparse
from huggingface_hub import hf_hub_download, snapshot_download
from transformers import AutoTokenizer
from private_gpt.paths import models_path, models_cache_path
from private_gpt.settings.settings import settings
resume_download = True
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='Setup: Download models from Hugging Face')
parser.add_argument('--resume', default=True, action=argparse.BooleanOptionalAction, help='Enable/Disable resume_download options to restart the download progress interrupted')
args = parser.parse_args()
resume_download = args.resume
os.makedirs(models_path, exist_ok=True)
# Download Embedding model
embedding_path = models_path / "embedding"
print(f"Downloading embedding {settings().huggingface.embedding_hf_model_name}")
snapshot_download(
repo_id=settings().huggingface.embedding_hf_model_name,
cache_dir=models_cache_path,
local_dir=embedding_path,
)
print("Embedding model downloaded!")
# Download LLM and create a symlink to the model file
print(f"Downloading LLM {settings().llamacpp.llm_hf_model_file}")
hf_hub_download(
repo_id=settings().llamacpp.llm_hf_repo_id,
filename=settings().llamacpp.llm_hf_model_file,
cache_dir=models_cache_path,
local_dir=models_path,
resume_download=resume_download,
)
print("LLM model downloaded!")
# Download Tokenizer
print(f"Downloading tokenizer {settings().llm.tokenizer}")
AutoTokenizer.from_pretrained(
pretrained_model_name_or_path=settings().llm.tokenizer,
cache_dir=models_cache_path,
)
print("Tokenizer downloaded!")
print("Setup done")
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