code: def get_model(args):
      config = CPMBeeConfig.from_json_file(args.model_config)
      model = CPMBee(config)
      model.config = config
      if args.load is not None:
          bmt.load(model, args.load)
      else:
          bmt.init_parameters(model)
      # insert LoRA
      if args.use_delta:
          delta_model = LoraModel(
              backbone_model=model,
              modified_modules=["project_q", "project_v"],
              backend="bmt",
          )
          delta_model.freeze_module(
              exclude=["deltas"], set_state_dict=True
          )
          delta_model.log()
      return model
  
imports: import time
  from typing import Dict, List, Union
  import torch
  import bmtrain
  import os
  from opendelta import LoraModel
  from cpm_live.arguments import get_args
  from cpm_live.models import CPMBee, CPMBeeConfig
  from cpm_live.tokenizers import CPMBeeTokenizer
  from cpm_live.utils import allgather_objects
  from cpm_live.training_tasks.bee import FinetuneDataset
label_desc: Use image node_id 2 for calling a global function with example usage: get_model(args) and returns: model