diff --git a/README.md b/README.md
index e0249fb..3614e97 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,6 @@ from lightning import Trainer
 from litmodels import download_model
 from litmodels.demos import BoringModel
 
-
 # Define the model name - this should be unique to your model
 # The format is <organization>/<teamspace>/<model-name>:<model-version>
 MY_MODEL_NAME = "jirka/kaggle/lit-boring-model:latest"
@@ -92,6 +91,54 @@ trainer = Trainer(max_epochs=4)
 trainer.fit(LitModel(), ckpt_path=checkpoint_path)
 ```
 
+You can also enhance your training with a simple Checkpointing callback which would always save the best model to the cloud storage and continue training.
+This can would be handy especially with long trainings or using interruptible machines so you would always resume/recover from the best model.
+
+```python
+import os
+import torch.utils.data as data
+import torchvision as tv
+from lightning import Callback, Trainer
+from litmodels import upload_model
+from litmodels.demos import BoringModel
+
+# Define the model name - this should be unique to your model
+# The format is <organization>/<teamspace>/<model-name>
+MY_MODEL_NAME = "jirka/kaggle/lit-auto-encoder-callback"
+
+
+class LitModel(BoringModel):
+    def training_step(self, batch, batch_idx: int):
+        loss = self.step(batch)
+        # logging the computed loss
+        self.log("train_loss", loss)
+        return {"loss": loss}
+
+
+class UploadModelCallback(Callback):
+    def on_train_epoch_end(self, trainer, pl_module):
+        # Get the best model path from the checkpoint callback
+        checkpoint_path = getattr(trainer.checkpoint_callback, "best_model_path")
+        if checkpoint_path and os.path.exists(checkpoint_path):
+            upload_model(model=checkpoint_path, name=MY_MODEL_NAME)
+
+
+dataset = tv.datasets.MNIST(".", download=True, transform=tv.transforms.ToTensor())
+train, val = data.random_split(dataset, [55000, 5000])
+
+trainer = Trainer(
+    max_epochs=2,
+    callbacks=[UploadModelCallback()],
+)
+trainer.fit(
+    LitModel(),
+    data.DataLoader(train, batch_size=256),
+    data.DataLoader(val, batch_size=256),
+)
+```
+
+## Logging Models
+
 You can also use model store together with [LitLogger](https://github.com/gridai/lit-logger) to log your model to the cloud storage.
 
 ```python