diff --git a/CHANGELOG.md b/CHANGELOG.md
index cee00b8e..1824cc1f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ## [Unreleased]
 
+### Added
+
+- During training, model checkpoints will now be saved at the end of each training epoch in addition to the checkpoints saved at the end of every validation run.
+
 ### Fixed
 
 - Precursor charges are now exported as integers instead of floats in the mzTab output file, in compliance with the mzTab specification.
diff --git a/casanovo/denovo/model_runner.py b/casanovo/denovo/model_runner.py
index 4bd2165e..d5acacb3 100644
--- a/casanovo/denovo/model_runner.py
+++ b/casanovo/denovo/model_runner.py
@@ -55,17 +55,22 @@ def __init__(
         self.writer = None
 
         # Configure checkpoints.
+        self.callbacks = [
+            ModelCheckpoint(
+                dirpath=config.model_save_folder_path,
+                save_on_train_epoch_end=True,
+            )
+        ]
+
         if config.save_top_k is not None:
-            self.callbacks = [
+            self.callbacks.append(
                 ModelCheckpoint(
                     dirpath=config.model_save_folder_path,
                     monitor="valid_CELoss",
                     mode="min",
                     save_top_k=config.save_top_k,
                 )
-            ]
-        else:
-            self.callbacks = None
+            )
 
     def __enter__(self):
         """Enter the context manager"""
@@ -187,7 +192,7 @@ def initialize_trainer(self, train: bool) -> None:
             additional_cfg = dict(
                 devices=devices,
                 callbacks=self.callbacks,
-                enable_checkpointing=self.config.save_top_k is not None,
+                enable_checkpointing=True,
                 max_epochs=self.config.max_epochs,
                 num_sanity_val_steps=self.config.num_sanity_val_steps,
                 strategy=self._get_strategy(),
diff --git a/tests/unit_tests/test_runner.py b/tests/unit_tests/test_runner.py
index 7febf3f7..2d0513bd 100644
--- a/tests/unit_tests/test_runner.py
+++ b/tests/unit_tests/test_runner.py
@@ -1,5 +1,7 @@
 """Unit tests specifically for the model_runner module."""
 
+from pathlib import Path
+
 import pytest
 import torch
 
@@ -10,6 +12,7 @@
 def test_initialize_model(tmp_path, mgf_small):
     """Test initializing a new or existing model."""
     config = Config()
+    config.model_save_folder_path = tmp_path
     # No model filename given, so train from scratch.
     ModelRunner(config=config).initialize_model(train=True)
 
@@ -149,3 +152,25 @@ def test_calculate_precision(tmp_path, mgf_small, tiny_config):
 
     assert "valid_aa_precision" in runner.model.history.columns
     assert "valid_pep_precision" in runner.model.history.columns
+
+
+def test_save_final_model(tmp_path, mgf_small, tiny_config):
+    """Test that final model checkpoints are saved."""
+    # Test checkpoint saving when val_check_interval is greater than training steps
+    config = Config(tiny_config)
+    config.val_check_interval = 50
+    model_file = tmp_path / "epoch=19-step=20.ckpt"
+    with ModelRunner(config) as runner:
+        runner.train([mgf_small], [mgf_small])
+
+    assert model_file.exists()
+    Path.unlink(model_file)
+
+    # Test checkpoint saving when val_check_interval is not a factor of training steps
+    config.val_check_interval = 15
+    validation_file = tmp_path / "epoch=14-step=15.ckpt"
+    with ModelRunner(config) as runner:
+        runner.train([mgf_small], [mgf_small])
+
+    assert model_file.exists()
+    assert validation_file.exists()