diff --git a/im2deep/__main__.py b/im2deep/__main__.py
index a01508b..fe91858 100644
--- a/im2deep/__main__.py
+++ b/im2deep/__main__.py
@@ -101,6 +101,13 @@ def setup_logging(passed_level):
     default=2,
     help="Charge state to use for calibration. Only used if calibrate_per_charge is set to False.",
 )
+@click.option(
+    "--use_single_model",
+    type=click.BOOL,
+    default=False,
+    help="Use a single model for prediction.",
+)
+
 def main(
     psm_file: str,
     calibration_file: Optional[str] = None,
@@ -108,6 +115,7 @@ def main(
     model_name: Optional[str] = "tims",
     log_level: Optional[str] = "info",
     n_jobs: Optional[int] = None,
+    use_single_model: Optional[bool] = False,
     calibrate_per_charge: Optional[bool] = True,
     use_charge_state: Optional[int] = 2,
 ):
@@ -185,6 +193,7 @@ def main(
             calibrate_per_charge=calibrate_per_charge,
             use_charge_state=use_charge_state,
             n_jobs=n_jobs,
+            use_single_model=use_single_model,
         )
     except IM2DeepError as e:
         LOGGER.error(e)
diff --git a/im2deep/calibrate.py b/im2deep/calibrate.py
index 5b028b3..9fb2987 100644
--- a/im2deep/calibrate.py
+++ b/im2deep/calibrate.py
@@ -84,7 +84,7 @@ def get_ccs_shift(
         """Calculating CCS shift based on {} overlapping peptide-charge pairs
         between PSMs and reference dataset""".format(both.shape[0])
     )
-    LOGGER.debug(both.columns)
+
     # How much CCS in calibration data is larger than reference CCS, so predictions
     # need to be increased by this amount
     return 0 if both.shape[0] == 0 else np.mean(both["ccs_observed"] - both["CCS"])
diff --git a/im2deep/im2deep.py b/im2deep/im2deep.py
index b1bc3d1..00e9894 100644
--- a/im2deep/im2deep.py
+++ b/im2deep/im2deep.py
@@ -20,6 +20,7 @@ def predict_ccs(
     model_name="tims",
     calibrate_per_charge=True,
     use_charge_state=2,
+    use_single_model=False,
     n_jobs=None,
     write_output=True,
 ):
@@ -31,6 +32,8 @@ def predict_ccs(
         path_model = Path(__file__).parent / "models" / "TIMS"
 
     path_model_list = list(path_model.glob("*.hdf5"))
+    if use_single_model:
+        path_model_list = [path_model_list[0]]
 
     dlc = DeepLC(path_model=path_model_list, n_jobs=n_jobs, predict_ccs=True)
     LOGGER.info("Predicting CCS values...")