code: def train(
      *,
      cfg: DictDefault,
      cli_args: TrainerCliArgs,
      dataset_meta: TrainDatasetMeta,
  ) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
      # load the tokenizer first
      LOG.debug(
          f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
          main_process_only=True,
      )
      tokenizer = load_tokenizer(cfg)
  
      train_dataset = dataset_meta.train_dataset
      eval_dataset = dataset_meta.eval_dataset
      total_num_steps = dataset_meta.total_num_steps
  
      # Load the model and tokenizer
      msg = "loading model"
      if cfg.adapter:
          msg += " and peft_config..."
      LOG.debug(msg)
      model, peft_config = load_model(
          cfg, tokenizer, inference=cli_args.inference
      )
      model_ref = None
      if cfg.rl:
          if cfg.adapter and not cfg.rl_adapter_ref_model:
              # use built-in trl autounwrap
              LOG.debug("Passing model_ref: None to RL trainer")
              model_ref = None  # explicit setting to None
          else:
              # load the model again for model_ref/baseline
              model_ref, _ = load_model(
                  cfg,
                  tokenizer,
                  inference=cli_args.inference,
                  reference_model=True,
              )
  
      safe_serialization = cfg.save_safetensors is True
  
      if (
          cfg.resume_from_checkpoint is None
          and cfg.auto_resume_from_checkpoints
      ):
          possible_checkpoints = [
              str(cp)
              for cp in Path(cfg.output_dir).glob("checkpoint-*")
          ]
          if len(possible_checkpoints) > 0:
              sorted_paths = sorted(
                  possible_checkpoints,
                  key=lambda path: int(path.split("-")[-1]),
              )
              cfg.resume_from_checkpoint = sorted_paths[-1]
              LOG.info(
                  f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
              )
      resume_from_checkpoint = cfg.resume_from_checkpoint
  
      if cfg.unfrozen_parameters:
          freeze_parameters_except(model, cfg.unfrozen_parameters)
  
      trainer = setup_trainer(
          cfg,
          train_dataset,
          eval_dataset,
          (model, model_ref),
          tokenizer,
          total_num_steps,
      )
  
      if hasattr(model, "config"):
          model.config.use_cache = False
  
      # go ahead and presave, so we have the adapter config available to inspect
      if peft_config:
          LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
          peft_config.save_pretrained(cfg.output_dir)
      # additionally presave the tokenizer and model configs
      if not Path(cfg.output_dir).is_dir():
          os.makedirs(cfg.output_dir, exist_ok=True)
      tokenizer.save_pretrained(str(Path(cfg.output_dir)))
      if hasattr(model, "config"):
          model.config.save_pretrained(str(Path(cfg.output_dir)))
  
      # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
      if cfg.local_rank == 0:
  
          def terminate_handler(_, __, model):
              if cfg.flash_optimum:
                  model = BetterTransformer.reverse(model)
              model.save_pretrained(
                  cfg.output_dir, safe_serialization=safe_serialization
              )
              sys.exit(0)
  
          signal.signal(
              signal.SIGINT,
              lambda signum, frame: terminate_handler(
                  signum, frame, model
              ),
          )
  
      badge_markdown = """[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)"""
      transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += (
          f"\n{badge_markdown}"
      )
  
      if getattr(cfg, "axolotl_config_path"):
          raw_axolotl_cfg = Path(cfg.axolotl_config_path)
          version = get_distribution("axolotl").version
          if raw_axolotl_cfg.is_file():
              transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"
  
      LOG.info("Starting trainer...")
      if cfg.group_by_length:
          LOG.info("hang tight... sorting dataset for group_by_length")
  
      pretrain_hooks(cfg, trainer)
      if cfg.flash_optimum:
          with torch.backends.cuda.sdp_kernel(
              enable_flash=True,
              enable_math=True,
              enable_mem_efficient=True,
          ):
              trainer.train(
                  resume_from_checkpoint=resume_from_checkpoint
              )
      else:
          trainer.train(resume_from_checkpoint=resume_from_checkpoint)
      post_train_hooks(cfg, trainer)
  
      LOG.info(
          f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}"
      )
  
      # post training
      for name, module in model.named_modules():
          if hasattr(module, "_post_training"):
              module._post_training(
                  model, name
              )  # pylint: disable=protected-access
  
      if trainer.is_fsdp_enabled:
          trainer.accelerator.state.fsdp_plugin.set_state_dict_type(
              "FULL_STATE_DICT"
          )
          LOG.info(
              "Set FSDP state dict type to FULL_STATE_DICT for saving."
          )
  
      if cfg.relora_steps:
          if cfg.adapter == "lora" and not (
              cfg.load_in_4bit or cfg.load_in_8bit
          ):
              model = model.merge_and_unload()
          else:
              # final model weights have already been saved by `ReLoRACallback.on_train_end`
              return model, tokenizer
  
      # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
      # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
      if cfg.fsdp:
          trainer.save_model(cfg.output_dir)
      elif cfg.deepspeed and is_deepspeed_zero3_enabled():
          # Copied over from: https://github.com/huggingface/accelerate/blob/5ae611118057232f441055f7ef9ba0b0f2b8d533/docs/source/usage_guides/deepspeed.md#saving-and-loading
          trainer.accelerator.wait_for_everyone()
          unwrapped_model = trainer.accelerator.unwrap_model(
              trainer.model_wrapped
          )
  
          # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if
          # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or
          # `zero3_save_16bit_model` is True in DeepSpeed Plugin.
          # For Zero Stages 1 and 2, models are saved as usual in the output directory.
          # The model name saved is `pytorch_model.bin`
          unwrapped_model.save_pretrained(
              cfg.output_dir,
              is_main_process=trainer.accelerator.is_main_process,
              save_function=trainer.accelerator.save,
              state_dict=trainer.accelerator.get_state_dict(
                  trainer.model_wrapped
              ),
          )
      elif cfg.local_rank == 0:
          if cfg.flash_optimum:
              model = BetterTransformer.reverse(model)
  
          model.save_pretrained(
              cfg.output_dir, safe_serialization=safe_serialization
          )
  
      if not cfg.hub_model_id:
          trainer.create_model_card(
              model_name=cfg.output_dir.lstrip("./")
          )
      elif cfg.hub_model_id:
          # defensively push to the hub to ensure the model card is updated
          trainer.push_to_hub()
  
      return model, tokenizer
  
imports: import os
  import signal
  import sys
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Optional, Tuple, Union
  import torch
  import transformers.modelcard
  from accelerate.logging import get_logger
  from datasets import Dataset
  from optimum.bettertransformer import BetterTransformer
  from peft import PeftModel
  from pkg_resources import get_distribution
  from transformers import PreTrainedModel, PreTrainedTokenizer
  from transformers.deepspeed import is_deepspeed_zero3_enabled
  from axolotl.common.cli import TrainerCliArgs
  from axolotl.logging_config import configure_logging
  from axolotl.utils.dict import DictDefault
  from axolotl.utils.freeze import freeze_parameters_except
  from axolotl.utils.models import load_model, load_tokenizer
  from axolotl.utils.trainer import setup_trainer
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: model,  tokenizer, model,  tokenizer