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- '''
- Converts a transformers model to safetensors format and shards it.
- This makes it faster to load (because of safetensors) and lowers its RAM usage
- while loading (because of sharding).
- Based on the original script by 81300:
- https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
- '''
- import argparse
- from pathlib import Path
- from sys import argv
- import torch
- from transformers import AutoModelForCausalLM
- from transformers import AutoTokenizer
-
- parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54))
- parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
- parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
- parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")
- args = parser.parse_args()
- if __name__ == '__main__':
- path = Path(args.MODEL)
- model_name = path.name
- print(f"Loading {model_name}...")
- model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.float16)
- tokenizer = AutoTokenizer.from_pretrained(path)
- out_folder = args.output or Path(f"models/{model_name}_safetensors")
- print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...")
- model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True)
- tokenizer.save_pretrained(out_folder)
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