code: def main():
      args = parse_args()
      model_path = args.model_name
      dataset_path = args.dataset_path
      output_path = args.output_path
      num_samples = args.num_samples
      seq_len = args.seq_len
  
      model, tokenizer = build_model_and_tokenizer(model_path)
      if not os.path.exists(dataset_path):
          raise FileNotFoundError(
              f"Cannot find the dataset at {args.dataset_path}"
          )
      dataset = load_dataset(
          "json", data_files=dataset_path, split="train"
      )
  
      model.quantized(
          tokenizer, dataset, num_samples=num_samples, seq_len=seq_len
      )
      model = model.cuda()
  
      model.save_quantized(output_path, model_basename="llama-7b")
  
file_path: colossalai/examples/inference/build_smoothquant_weight.py
imports: import argparse
  import os
  import torch
  from datasets import load_dataset
  from transformers import LlamaTokenizer
  from colossalai.inference.quant.smoothquant.models.llama import SmoothLlamaForCausalLM
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types