diff --git a/src/osc_transformer_based_extractor/kpi_detection/cli_kpi_detection.py b/src/osc_transformer_based_extractor/kpi_detection/cli_kpi_detection.py
index 8cae7d4..e9344d1 100644
--- a/src/osc_transformer_based_extractor/kpi_detection/cli_kpi_detection.py
+++ b/src/osc_transformer_based_extractor/kpi_detection/cli_kpi_detection.py
@@ -1,4 +1,5 @@
 import typer
+import os
 from .train_kpi_detection import (
     train_kpi_detection,
     check_output_dir,
@@ -43,6 +44,7 @@ def fine_tune_qna(
     output_dir: str = typer.Argument(
         ..., help="Directory to save the fine-tuned model."
     ),
+    export_model_name: str = typer.Argument(..., help="Name of the model to export."),
     save_steps: int = typer.Argument(
         ..., help="Number of steps between saving model checkpoints."
     ),
@@ -59,10 +61,13 @@ def fine_tune_qna(
         batch_size=batch_size,
         learning_rate=learning_rate,
         output_dir=output_dir,
+        export_model_name=export_model_name,
         save_steps=save_steps,
     )
-
-    typer.echo(f"Model '{model_name}' trained and saved successfully at {output_dir}")
+    saved_model_path = os.path.join(output_dir, f"{export_model_name}")
+    typer.echo(
+        f"Model '{model_name}' is trained and saved successfully at {saved_model_path}"
+    )
 
 
 @kpi_detection_app.command("inference")
@@ -76,6 +81,7 @@ def inference_qna(
     model_path: str = typer.Argument(
         ..., help="Path to the pre-trained model directory OR name on huggingface."
     ),
+    batch_size: int = typer.Argument(16, help="The batch size for inference."),
 ):
     """Perform inference using a pre-trained model on a dataset of kpis and contexts, saving an output Excel file."""
     try:
@@ -86,6 +92,7 @@ def inference_qna(
             data_file_path=data_file_path,
             output_path=output_path,
             model_path=model_path,
+            batch_size=batch_size,
         )
 
         typer.echo("Inference completed successfully!")