diff --git a/src/axolotl/train.py b/src/axolotl/train.py
index 515248fce..eb2ced231 100644
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -6,15 +6,15 @@ import signal
 import sys
 import weakref
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Any, Tuple, Union
 
 import torch
 import transformers.modelcard
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
-from peft import PeftModel
+from peft import PeftConfig, PeftModel
 from pkg_resources import get_distribution  # type: ignore
-from transformers import PreTrainedModel, PreTrainedTokenizer
+from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 
 from axolotl.common.datasets import TrainDatasetMeta
@@ -27,11 +27,13 @@ from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
+# Optional imports with graceful fallbacks
 try:
     from optimum.bettertransformer import BetterTransformer
 except ImportError:
     BetterTransformer = None
 
+# Project setup
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
@@ -40,9 +42,20 @@ configure_logging()
 LOG = get_logger(__name__)
 
 
-def train(
-    *, cfg: DictDefault, dataset_meta: TrainDatasetMeta
-) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
+def setup_model_and_tokenizer(
+    cfg: DictDefault,
+) -> Tuple[
+    PreTrainedTokenizer, ProcessorMixin | None, PreTrainedModel, PeftConfig | None
+]:
+    """
+    Load the tokenizer, processor (for multimodal models), and model based on configuration.
+
+    Args:
+        cfg: The configuration dictionary with training parameters
+
+    Returns:
+        Tuple containing tokenizer, processor (if multimodal, else None), model, and peft_config (if applicable, else None)
+    """
     # Load tokenizer
     LOG.debug(
         f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
@@ -55,11 +68,58 @@ def train(
     if cfg.is_multimodal:
         processor = load_processor(cfg, tokenizer)
 
-    # Get datasets
-    train_dataset = dataset_meta.train_dataset
-    eval_dataset = dataset_meta.eval_dataset
-    total_num_steps = dataset_meta.total_num_steps
+    # Load the model and peft_config
+    msg = "loading model"
+    if cfg.adapter:
+        msg += " and peft_config..."
+    LOG.debug(msg)
 
+    model, peft_config = load_model(cfg, tokenizer, processor=processor)
+    if model.generation_config is not None:
+        model.generation_config.do_sample = True
+
+    # Apply freezing if specified
+    if cfg.unfrozen_parameters:
+        freeze_layers_except(model, cfg.unfrozen_parameters)
+
+    return tokenizer, processor, model, peft_config
+
+
+def setup_reference_model(
+    cfg: DictDefault, tokenizer: PreTrainedTokenizer
+) -> PreTrainedModel | None:
+    """
+    Set up the reference model for RL training if needed.
+
+    Args:
+        cfg: The configuration dictionary
+        tokenizer: The tokenizer to use for the reference model
+
+    Returns:
+        Reference model if needed for RL training, None otherwise
+    """
+    model_ref = None
+    if cfg.rl and cfg.rl != "orpo":
+        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, reference_model=True)
+    return model_ref
+
+
+def determine_resume_checkpoint(cfg: DictDefault) -> str | None:
+    """
+    Determine the checkpoint to resume from based on configuration.
+
+    Args:
+        cfg: The configuration dictionary
+
+    Returns:
+        Path to the checkpoint to resume from, or None if not resuming
+    """
     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-*")
@@ -73,77 +133,22 @@ def train(
             LOG.info(
                 f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
             )
-    resume_from_checkpoint = cfg.resume_from_checkpoint
+    return cfg.resume_from_checkpoint
 
-    # 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, processor=processor)
-    if model.generation_config is not None:
-        model.generation_config.do_sample = True
 
-    model_ref = None
-    if cfg.rl and cfg.rl != "orpo":
-        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, reference_model=True)
+def setup_signal_handler(
+    cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
+):
+    """
+    Set up signal handler for graceful termination.
 
-    safe_serialization = cfg.save_safetensors is True
-
-    if cfg.unfrozen_parameters:
-        freeze_layers_except(model, cfg.unfrozen_parameters)
-
-    trainer = setup_trainer(
-        cfg,
-        train_dataset,
-        eval_dataset,
-        (model, model_ref, peft_config),
-        tokenizer,
-        processor,
-        total_num_steps,
-    )
-
-    if cfg.fix_untrained_tokens:
-        # check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
-        sig = inspect.signature(fix_untrained_tokens)
-        # if the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
-        if "token_ids_to_fix" in sig.parameters and isinstance(
-            cfg.fix_untrained_tokens, list
-        ):
-            fix_untrained_tokens(
-                model,
-                tokenizer,
-                train_dataset,
-                token_ids_to_fix=cfg.fix_untrained_tokens,
-            )
-        else:
-            fix_untrained_tokens(model, tokenizer, train_dataset)
-        if cfg.local_rank == 0:
-            model.save_pretrained(
-                str(Path(cfg.output_dir)), safe_serialization=safe_serialization
-            )
-
-    # 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 and not cfg.use_ray
-    ):  # ray workers don't have access to this signal
+    Args:
+        cfg: The configuration dictionary
+        model: The model to save on termination
+        safe_serialization: Whether to use safe serialization when saving
+    """
+    # ray workers don't have access to this signal
+    if cfg.local_rank == 0 and not cfg.use_ray:
 
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None:
@@ -161,21 +166,22 @@ def train(
             lambda signum, frame: terminate_handler(signum, frame, _model_weakref),
         )
 
-    badge_markdown = """[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/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"
+def execute_training(
+    cfg: DictDefault, trainer: Any, resume_from_checkpoint: str | None
+):
+    """
+    Execute the training process with appropriate backend configurations.
 
+    Args:
+        cfg: The configuration dictionary
+        trainer: The configured trainer object
+        resume_from_checkpoint: Path to checkpoint to resume from, if applicable
+    """
     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(
             # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
@@ -187,15 +193,30 @@ def train(
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
-    post_train_hooks(cfg, trainer)
 
+def save_trained_model(
+    cfg: DictDefault,
+    trainer: Any,
+    model: PreTrainedModel,
+    safe_serialization: bool,
+):
+    """
+    Save the trained model according to configuration and training setup.
+
+    Args:
+        cfg: The configuration dictionary
+        trainer: The trainer object
+        model: The trained model to save
+        safe_serialization: Whether to use safe serialization
+    """
     LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
 
-    # post training
+    # Post training module hooks
     for name, module in model.named_modules():
         if hasattr(module, "_post_training"):
             module._post_training(model, name)  # pylint: disable=protected-access
 
+    # Handle FSDP state dict type
     state_dict_type = "FULL_STATE_DICT"
     if trainer.is_fsdp_enabled:
         if cfg.fsdp_final_state_dict_type:
@@ -203,12 +224,13 @@ def train(
         trainer.accelerator.state.fsdp_plugin.set_state_dict_type(state_dict_type)
         LOG.info(f"Set FSDP state dict type to {state_dict_type} for saving.")
 
+    # Handle ReLoRA early return case
     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
+            return
 
     # 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
@@ -255,6 +277,15 @@ def train(
             )
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
+
+def create_model_card(cfg: DictDefault, trainer: Any):
+    """
+    Create a model card for the trained model if needed.
+
+    Args:
+        cfg: The configuration dictionary
+        trainer: The trainer object with model card creation capabilities
+    """
     if not cfg.hub_model_id:
         try:
             model_card_kwarg = {
@@ -291,22 +322,162 @@ def train(
         # defensively push to the hub to ensure the model card is updated
         trainer.push_to_hub()
 
+
+def save_initial_configs(
+    cfg: DictDefault,
+    tokenizer: PreTrainedTokenizer,
+    model: PreTrainedModel,
+    peft_config: PeftConfig | None,
+):
+    """
+    Save initial configurations before training.
+
+    Args:
+        cfg: The configuration dictionary
+        tokenizer: The tokenizer to save
+        model: The model to save configuration for
+        peft_config: The PEFT configuration to save if applicable
+    """
+    # 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
+    output_dir = Path(cfg.output_dir)
+    if not output_dir.is_dir():
+        os.makedirs(cfg.output_dir, exist_ok=True)
+
+    tokenizer.save_pretrained(str(output_dir))
+    if hasattr(model, "config"):
+        model.config.save_pretrained(str(output_dir))
+
+
+def setup_badge_for_model_card():
+    """Set up the Axolotl badge for the model card."""
+    badge_markdown = """[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)"""
+    transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
+
+
+def add_config_to_model_card(cfg: DictDefault):
+    """
+    Add the Axolotl configuration to the model card if available.
+
+    Args:
+        cfg: The configuration dictionary with path to axolotl config file
+    """
+    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"
+
+
+def handle_untrained_tokens_fix(
+    cfg: DictDefault,
+    model: PreTrainedModel,
+    tokenizer: PreTrainedTokenizer,
+    train_dataset: Any,
+    safe_serialization: bool,
+):
+    """
+    Apply fixes for untrained tokens if configured.
+
+    Args:
+        cfg: The configuration dictionary
+        model: The model to apply fixes to
+        tokenizer: The tokenizer for token identification
+        train_dataset: The training dataset to analyze
+        safe_serialization: Whether to use safe serialization when saving
+    """
+    if not cfg.fix_untrained_tokens:
+        return
+
+    # check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
+    sig = inspect.signature(fix_untrained_tokens)
+
+    # if the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
+    if "token_ids_to_fix" in sig.parameters and isinstance(
+        cfg.fix_untrained_tokens, list
+    ):
+        fix_untrained_tokens(
+            model,
+            tokenizer,
+            train_dataset,
+            token_ids_to_fix=cfg.fix_untrained_tokens,
+        )
+    else:
+        fix_untrained_tokens(model, tokenizer, train_dataset)
+
+    if cfg.local_rank == 0:
+        model.save_pretrained(
+            str(Path(cfg.output_dir)), safe_serialization=safe_serialization
+        )
+
+
+def train(
+    *, cfg: DictDefault, dataset_meta: TrainDatasetMeta
+) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
+    """
+    Train a model on the given dataset.
+
+    Args:
+        cfg: The configuration dictionary with training parameters
+        dataset_meta: Metadata about the training dataset
+
+    Returns:
+        Tuple of (model, tokenizer) after training
+    """
+    # Load tokenizer, processor and model
+    tokenizer, processor, model, peft_config = setup_model_and_tokenizer(cfg)
+
+    # Set up reference model for RL if needed
+    model_ref = setup_reference_model(cfg, tokenizer)
+
+    # Determine if we need to resume from a checkpoint
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+
+    # Get datasets from metadata
+    train_dataset = dataset_meta.train_dataset
+    eval_dataset = dataset_meta.eval_dataset
+    total_num_steps = dataset_meta.total_num_steps
+
+    # Configuration for saving
+    safe_serialization = cfg.save_safetensors is True
+
+    # Set up trainer
+    trainer = setup_trainer(
+        cfg,
+        train_dataset,
+        eval_dataset,
+        (model, model_ref, peft_config),
+        tokenizer,
+        processor,
+        total_num_steps,
+    )
+
+    # Handle untrained tokens if configured
+    handle_untrained_tokens_fix(
+        cfg, model, tokenizer, train_dataset, safe_serialization
+    )
+
+    # Save initial configs
+    save_initial_configs(cfg, tokenizer, model, peft_config)
+
+    # Set up signal handler for graceful termination
+    setup_signal_handler(cfg, model, safe_serialization)
+
+    # Set up badges and config info for model card
+    setup_badge_for_model_card()
+    add_config_to_model_card(cfg)
+
+    # Execute the training
+    execute_training(cfg, trainer, resume_from_checkpoint)
+
+    # Save the trained model
+    save_trained_model(cfg, trainer, model, safe_serialization)
+
+    # Create model card
+    create_model_card(cfg, trainer)
+
     return model, tokenizer
-
-
-def pretrain_hooks(_cfg, _trainer):
-    """
-    Run hooks right before kicking off the training
-    :param cfg:
-    :param trainer:
-    :return:
-    """
-
-
-def post_train_hooks(_cfg, _trainer):
-    """
-    Run hooks right after training completes
-    :param cfg:
-    :param trainer:
-    :return:
-    """