diff --git a/pyproject.toml b/pyproject.toml
index 91c20d4..e78fca4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ urls.Source = "https://github.com/theislab/multigrate"
 urls.Home-page = "https://github.com/theislab/multigrate"
 dependencies = [
     "scanpy",
-    "scvi-tools>=0.19.0",
+    "scvi-tools<1.0.0",
     "matplotlib"
 ]
 
diff --git a/src/multigrate/model/_multivae.py b/src/multigrate/model/_multivae.py
index 4419a11..5498baf 100644
--- a/src/multigrate/model/_multivae.py
+++ b/src/multigrate/model/_multivae.py
@@ -17,6 +17,7 @@
 from scvi.model.base._utils import _initialize_model
 from scvi.train import AdversarialTrainingPlan, TrainRunner
 from scvi.train._callbacks import SaveBestState
+from pytorch_lightning.callbacks import ModelCheckpoint
 
 from ..dataloaders import GroupDataSplitter
 from ..module import MultiVAETorch
@@ -224,6 +225,8 @@ def train(
         n_steps_kl_warmup: Optional[int] = None,
         adversarial_mixing: bool = False,
         plan_kwargs: Optional[dict] = None,
+        save_checkpoint_every_n_epochs: Optional[int] = None,
+        path_to_checkpoints: Optional[str] = None,
         **kwargs,
     ):
         """Train the model using amortized variational inference.
@@ -287,6 +290,19 @@ def train(
                 kwargs["callbacks"] = []
             kwargs["callbacks"].append(SaveBestState(monitor="reconstruction_loss_validation"))
 
+        if save_checkpoint_every_n_epochs is not None:
+            if path_to_checkpoints is not None:
+                kwargs["callbacks"].append(ModelCheckpoint(
+                    dirpath = path_to_checkpoints,
+                    save_top_k = -1,
+                    monitor = 'epoch',
+                    every_n_epochs = save_checkpoint_every_n_epochs,
+                    verbose = True,
+                ))
+            else:
+                raise ValueError(f"`save_checkpoint_every_n_epochs` = {save_checkpoint_every_n_epochs} so `path_to_checkpoints` has to be not None but is {path_to_checkpoints}.")
+
+
         if self.group_column is not None:
             data_splitter = GroupDataSplitter(
                 self.adata_manager,
@@ -325,6 +341,7 @@ def train(
             check_val_every_n_epoch=check_val_every_n_epoch,
             early_stopping_monitor="reconstruction_loss_validation",
             early_stopping_patience=50,
+            enable_checkpointing=True,
             **kwargs,
         )
         return runner()