diff --git a/scripts/finetune.py b/scripts/finetune.py
index 944876a33..fe1d5b8e9 100644
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -19,16 +19,11 @@ from transformers import GenerationConfig, TextStreamer
 
 from axolotl.logging_config import configure_logging
 from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
+from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import (
-    calculate_total_num_steps,
-    process_datasets_for_packing,
-    setup_trainer,
-)
+from axolotl.utils.trainer import setup_trainer
 from axolotl.utils.wandb import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -39,7 +34,6 @@ configure_logging()
 LOG = logging.getLogger("axolotl.scripts")
 
 
-DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
@@ -183,32 +177,7 @@ def train(
     if (
         check_not_in(["shard", "merge_lora"], kwargs) and not cfg.inference
     ):  # don't need to load dataset for these
-        if not cfg.pretraining_dataset:
-            train_dataset, eval_dataset = load_prepare_datasets(
-                tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
-            )
-        else:
-            train_dataset = load_pretraining_dataset(
-                cfg.pretraining_dataset,
-                tokenizer,
-                max_tokens=cfg.sequence_len,
-                seed=cfg.seed or 42,
-            )
-            # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
-            train_dataset = train_dataset.with_format("torch")
-            eval_dataset = None
-
-        with zero_first(is_main_process()):
-            train_dataset, eval_dataset = process_datasets_for_packing(
-                cfg, train_dataset, eval_dataset
-            )
-        if cfg.max_steps:
-            total_num_steps = min(
-                calculate_total_num_steps(cfg, train_dataset, tokenizer), cfg.max_steps
-            )
-            LOG.info(f"Maximum number of steps set at {total_num_steps}")
-        else:
-            total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
+        train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
 
     if cfg.debug or "debug" in kwargs:
         LOG.info("check_dataset_labels...")
diff --git a/src/axolotl/utils/data.py b/src/axolotl/utils/data.py
index 6ebfb9d17..d64b06a10 100644
--- a/src/axolotl/utils/data.py
+++ b/src/axolotl/utils/data.py
@@ -42,8 +42,43 @@ from axolotl.prompters import (
     SummarizeTLDRPrompter,
 )
 from axolotl.utils.distributed import is_main_process, zero_first
+from axolotl.utils.trainer import (
+    calculate_total_num_steps,
+    process_datasets_for_packing,
+)
 
 LOG = logging.getLogger("axolotl")
+DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
+
+
+def prepare_dataset(cfg, tokenizer):
+    if not cfg.pretraining_dataset:
+        train_dataset, eval_dataset = load_prepare_datasets(
+            tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+        )
+    else:
+        train_dataset = load_pretraining_dataset(
+            cfg.pretraining_dataset,
+            tokenizer,
+            max_tokens=cfg.sequence_len,
+            seed=cfg.seed or 42,
+        )
+        # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
+        train_dataset = train_dataset.with_format("torch")
+        eval_dataset = None
+
+    with zero_first(is_main_process()):
+        train_dataset, eval_dataset = process_datasets_for_packing(
+            cfg, train_dataset, eval_dataset
+        )
+    if cfg.max_steps:
+        total_num_steps = min(
+            calculate_total_num_steps(cfg, train_dataset, tokenizer), cfg.max_steps
+        )
+        LOG.info(f"Maximum number of steps set at {total_num_steps}")
+    else:
+        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
+    return train_dataset, eval_dataset, total_num_steps
 
 
 def load_tokenized_prepared_datasets(