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