From 44e569c3a260d08c455b20103d38ad7aa0f87587 Mon Sep 17 00:00:00 2001 From: oobabooga <112222186+oobabooga@users.noreply.github.com> Date: Sun, 2 Feb 2025 16:15:33 -0800 Subject: [PATCH] Remove obsolete convert-to-safetensors.py from the repository --- convert-to-safetensors.py | 38 -------------------------------------- 1 file changed, 38 deletions(-) delete mode 100644 convert-to-safetensors.py diff --git a/convert-to-safetensors.py b/convert-to-safetensors.py deleted file mode 100644 index 3b721e7c..00000000 --- a/convert-to-safetensors.py +++ /dev/null @@ -1,38 +0,0 @@ -''' - -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 - -import torch -from transformers import AutoModelForCausalLM, 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).") -parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') -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.bfloat16 if args.bf16 else 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)