diff --git a/examples/cars/train.py b/examples/cars/train.py
index 5131979b..630b3c05 100644
--- a/examples/cars/train.py
+++ b/examples/cars/train.py
@@ -35,6 +35,7 @@ def train(
 
     Quaterion.fit(
         trainable_model=model,
+        trainer=None,
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
     )
diff --git a/quaterion/main.py b/quaterion/main.py
index bf5b4fda..220d6873 100644
--- a/quaterion/main.py
+++ b/quaterion/main.py
@@ -28,9 +28,9 @@ class Quaterion:
     def fit(
         cls,
         trainable_model: TrainableModel,
+        trainer: Optional[pl.Trainer],
         train_dataloader: SimilarityDataLoader,
         val_dataloader: Optional[SimilarityDataLoader] = None,
-        trainer: Optional[pl.Trainer] = None,
         ckpt_path: Optional[str] = None,
     ):
         """Handle training routine
@@ -39,12 +39,15 @@ def fit(
 
         Args:
             trainable_model: model to fit
+            trainer:
+                `pytorch_lightning.Trainer` instance to handle fitting routine internally.
+                If `None` passed, trainer will be created with :meth:`Quaterion.trainer_defaults`.
+                The default parameters are intended to serve as a quick start for learning the model, and we
+                encourage users to try different parameters if the default ones do not give a satisfactory result.
             train_dataloader: DataLoader instance to retrieve samples during training
                 stage
             val_dataloader: Optional DataLoader instance to retrieve samples during
                 validation stage
-            trainer: `pytorch_lightning.Trainer` instance to handle fitting routine
-                internally. If not passed will be created with `Quaterion.trainer_defaults`
             ckpt_path: Path/URL of the checkpoint from which training is resumed. If there is
                 no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
                 training will start from the beginning of the next epoch.
@@ -132,6 +135,21 @@ def trainer_defaults(
         regular deep learning model training.
         This default parameters may be overwritten, if you need some special behaviour for your special task.
 
+        Consider overriding default parameters if you need to adjust Trainer parameters:
+
+        Example::
+
+            trainer_kwargs = Quaterion.trainer_defaults(
+                trainable_model=model,
+                train_dataloader=train_dataloader
+            )
+            trainer_kwargs['logger'] = pl.loggers.WandbLogger(
+                name="example_model",
+                project="example_project",
+            )
+            trainer_kwargs['callbacks'].append(YourCustomCallback())
+            trainer = pl.Trainer(**trainer_kwargs)
+
         Args:
             trainable_model: We will try to adjust default params based on model configuration, if provided
             train_dataloader: If provided, trainer params will be adjusted according to dataset