diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 539c97d..5974a29 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -45,13 +45,13 @@ repos:
       - id: isort
 
   -   repo: https://github.com/psf/black.git
-      rev: '21.12b0'
+      rev: '22.1.0'
       hooks:
       -   id: black
       -   id: black-jupyter
 
   - repo: https://github.com/asottile/blacken-docs.git
-    rev: 'v1.12.0'
+    rev: 'v1.12.1'
     hooks:
     - id: blacken-docs
 
@@ -59,8 +59,6 @@ repos:
     rev: '4.0.1'
     hooks:
       - id: flake8
-        additional_dependencies:
-          - flake8-docstrings==1.6.0
 
   - repo: https://github.com/pycqa/pydocstyle.git
     rev: '6.1.1'
diff --git a/src/nn_core/serialization.py b/src/nn_core/serialization.py
index 1d8d366..893d665 100644
--- a/src/nn_core/serialization.py
+++ b/src/nn_core/serialization.py
@@ -10,6 +10,7 @@
 from typing import Any, Callable, Dict, Optional, Type, Union
 
 import pytorch_lightning as pl
+import torch
 from pytorch_lightning.plugins import TorchCheckpointIO
 
 METADATA_KEY: str = "metadata"
@@ -123,8 +124,15 @@ def extract_checkpoint(ckpt_file: Path) -> Path:
         yield Path(tmp_dir)
 
 
-def load_model(module_class: Type[pl.LightningModule], checkpoint_path: Path):
-    checkpoint = NNCheckpointIO.load(path=checkpoint_path)
-
-    model = module_class._load_model_state(checkpoint=checkpoint, metadata=checkpoint["metadata"])
-    return model
+def load_model(
+    module_class: Type[pl.LightningModule],
+    checkpoint_path: Path,
+    map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
+):
+    # Lightning checkpoints end with .ckpt, ours with .ckpt.zip
+    if checkpoint_path.name.endswith(".ckpt.zip"):
+        checkpoint = NNCheckpointIO.load(path=checkpoint_path, map_location=map_location)
+        return module_class._load_model_state(checkpoint=checkpoint, metadata=checkpoint.get("metadata", None))
+    else:
+        pylogger.warning(f"Loading a legacy checkpoint (from vanilla PyTorch Lightning): '{checkpoint_path}'")
+        module_class.load_from_checkpoint(checkpoint_path=str(checkpoint_path), map_location=map_location)