diff --git a/sub-packages/bionemo-esm2/src/bionemo/esm2/model/finetune/train.py b/sub-packages/bionemo-esm2/src/bionemo/esm2/model/finetune/train.py
index 638729e3f..c5a517c31 100644
--- a/sub-packages/bionemo-esm2/src/bionemo/esm2/model/finetune/train.py
+++ b/sub-packages/bionemo-esm2/src/bionemo/esm2/model/finetune/train.py
@@ -13,12 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
+import os
 import tempfile
 from pathlib import Path
 from typing import Sequence, Tuple
 
 import lightning.pytorch as pl
+import pandas as pd
 from lightning.pytorch.callbacks import Callback, RichModelSummary
 from lightning.pytorch.loggers import TensorBoardLogger
 from megatron.core.optimizer.optimizer_config import OptimizerConfig
@@ -149,7 +150,8 @@ def train_model(
 
 if __name__ == "__main__":
     # set the results directory
-    experiment_results_dir = tempfile.TemporaryDirectory().name
+    temp_dir = tempfile.mkdtemp()
+    print(f"Temporary directory created at: {temp_dir}")
 
     # create a List[Tuple] with (sequence, target) values
     artificial_sequence_data = [
@@ -165,9 +167,14 @@ def train_model(
         "SGSKASSDSQDANQCCTSCEDNAPATSYCVECSEPLCETCVEAHQRVKYTKDHTVRSTGPAKT",
     ]
     data = [(seq, len(seq) / 100.0) for seq in artificial_sequence_data]
+    # Create a DataFrame
+    df = pd.DataFrame(data, columns=["sequences", "labels"])
+    # Save the DataFrame to a CSV file
+    csv_file = os.path.join(temp_dir, "protein_dataset.csv")
+    df.to_csv(csv_file, index=False)
 
     # we are training and validating on the same dataset for simplicity
-    dataset = InMemorySingleValueDataset(data)
+    dataset = InMemorySingleValueDataset(csv_file)
     data_module = ESM2FineTuneDataModule(train_dataset=dataset, valid_dataset=dataset)
 
     experiment_name = "finetune_regressor"
@@ -181,7 +188,7 @@ def train_model(
 
     checkpoint, metrics, trainer = train_model(
         experiment_name=experiment_name,
-        experiment_dir=Path(experiment_results_dir),  # new checkpoint will land in a subdir of this
+        experiment_dir=Path(temp_dir),  # new checkpoint will land in a subdir of this
         config=config,  # same config as before since we are just continuing training
         data_module=data_module,
         n_steps_train=n_steps_train,