code: def get_model_config(model_name):
      if "175b" in model_name:
          config = AutoConfig.from_pretrained("facebook/opt-66b")
          config.hidden_size = 12288
          config.word_embed_proj_dim = 12288
          config.ffn_dim = 12288 * 4
          config.num_attention_heads = 96
          config.num_hidden_layers = 96
      else:
          config = AutoConfig.from_pretrained(model_name)
  
      return config
  
file_path: H2O/h2o_flexgen/benchmark/hf_ds/hf_opt.py
imports: import argparse
  import multiprocessing
  import os
  import pickle
  import time
  import numpy
  from accelerate import infer_auto_device_map, init_empty_weights, load_checkpoint_and_dispatch
  from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
  from transformers import OPTForCausalLM
  import torch
  from flexgen.timer import timers
  from flexgen.utils import GB, project_decode_latency, write_benchmark_log
  from flexgen.opt_config import get_opt_config, disable_torch_init, disable_hf_opt_init
label_desc: Use image node_id 4 for calling a global function with example usage: get_model_config(model_name) and returns: config