code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model",
          type=str,
          default="facebook/opt-125m",
          help="model name",
      )
      parser.add_argument(
          "--save-path",
          type=str,
          default=None,
          help="smoothed model model save path",
      )
      parser.add_argument("--num-samples", type=int, default=512)
      parser.add_argument("--seq-len", type=int, default=512)
      parser.add_argument(
          "--device",
          type=str,
          default=None,
          help="The device to use for generation.",
      )
      args = parser.parse_args()
  
      if args.device is None:
          if torch.cuda.is_available():
              device = torch.device("cuda")
          elif torch.backends.mps.is_available():
              device = torch.device("mps")
          else:
              device = torch.device("cpu")
      else:
          device = torch.device(args.device)
  
      dataset = load_dataset(
          "lambada", split=f"validation[:{args.num_samples}]"
      ).shuffle()
      tokenizer = AutoTokenizer.from_pretrained(
          args.model, model_max_length=args.seq_len
      )
      model = AutoModelForCausalLM.from_pretrained(
          args.model, torch_dtype="auto"
      ).to(device)
  
      act_scales = get_act_scales(
          model, tokenizer, dataset, args.num_samples, args.seq_len
      )
      smooth_lm(model, act_scales, 0.5)
      save_path = args.save_path
      if save_path is None:
          save_path = os.path.join("smoothed_models", args.model)
      model.save_pretrained(save_path)
      tokenizer.save_pretrained(save_path)
  
file_path: quanto/external/smoothquant/smoothquant.py
imports: import argparse
  import functools
  import os
  import torch
  import torch.nn
  from datasets import load_dataset
  from tqdm import tqdm
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from transformers.models.bloom.modeling_bloom import BloomBlock
  from transformers.models.opt.modeling_opt import OPTDecoderLayer
  from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRMSNorm
  from transformers.models.mistral.modeling_mistral import MistralDecoderLayer, MistralRMSNorm
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types