diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 384ef2229..aa4ad710b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0  # Use the ref you want to point at
+    rev: v5.0.0  # Use the ref you want to point at
     hooks:
     - id: trailing-whitespace
     - id: check-ast
@@ -16,7 +16,7 @@ repos:
     - id: check-toml
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.6.5'
+    rev: 'v0.8.0'
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]
@@ -25,7 +25,7 @@ repos:
       types_or: [python, jupyter]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.2
+    rev: v1.13.0
     hooks:
     - id: mypy
       entry: python3 -m mypy --config-file pyproject.toml
@@ -41,7 +41,7 @@ repos:
       entry: python3 -m nbstripout
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.8.7
+    rev: 1.9.1
     hooks:
     - id: nbqa-black
     - id: nbqa-ruff
diff --git a/cyclops/data/features/medical_image.py b/cyclops/data/features/medical_image.py
index 7b7d9cae9..3dcfd9583 100644
--- a/cyclops/data/features/medical_image.py
+++ b/cyclops/data/features/medical_image.py
@@ -209,11 +209,14 @@ def decode_example(
                     use_auth_token = token_per_repo_id.get(repo_id)
                 except ValueError:
                     use_auth_token = None
-                with xopen(
-                    path,
-                    "rb",
-                    use_auth_token=use_auth_token,
-                ) as file_obj, BytesIO(file_obj.read()) as buffer:
+                with (
+                    xopen(
+                        path,
+                        "rb",
+                        use_auth_token=use_auth_token,
+                    ) as file_obj,
+                    BytesIO(file_obj.read()) as buffer,
+                ):
                     image, metadata = self._read_file_from_bytes(buffer)
                     metadata["filename_or_obj"] = path
 
diff --git a/cyclops/evaluate/utils.py b/cyclops/evaluate/utils.py
index 835de6263..1bbe99d1e 100644
--- a/cyclops/evaluate/utils.py
+++ b/cyclops/evaluate/utils.py
@@ -159,9 +159,11 @@ def get_columns_as_array(
     if isinstance(columns, str):
         columns = [columns]
 
-    with dataset.formatted_as("arrow", columns=columns, output_all_columns=True) if (
-        isinstance(dataset, Dataset) and dataset.format != "arrow"
-    ) else nullcontext():
+    with (
+        dataset.formatted_as("arrow", columns=columns, output_all_columns=True)
+        if (isinstance(dataset, Dataset) and dataset.format != "arrow")
+        else nullcontext()
+    ):
         out_arr = squeeze_all(
             xp.stack(
                 [xp.asarray(dataset[col].to_pylist()) for col in columns], axis=-1
diff --git a/cyclops/models/wrappers/pt_model.py b/cyclops/models/wrappers/pt_model.py
index 50b3a4302..37499dec2 100644
--- a/cyclops/models/wrappers/pt_model.py
+++ b/cyclops/models/wrappers/pt_model.py
@@ -968,14 +968,17 @@ def fit(
                 splits_mapping["validation"] = val_split
 
                 format_kwargs = {} if transforms is None else {"transform": transforms}
-                with X[train_split].formatted_as(
-                    "custom" if transforms is not None else "torch",
-                    columns=feature_columns + target_columns,
-                    **format_kwargs,
-                ), X[val_split].formatted_as(
-                    "custom" if transforms is not None else "torch",
-                    columns=feature_columns + target_columns,
-                    **format_kwargs,
+                with (
+                    X[train_split].formatted_as(
+                        "custom" if transforms is not None else "torch",
+                        columns=feature_columns + target_columns,
+                        **format_kwargs,
+                    ),
+                    X[val_split].formatted_as(
+                        "custom" if transforms is not None else "torch",
+                        columns=feature_columns + target_columns,
+                        **format_kwargs,
+                    ),
                 ):
                     self.partial_fit(
                         X,
@@ -1309,7 +1312,7 @@ def save_model(self, filepath: str, overwrite: bool = True, **kwargs):
         if include_lr_scheduler:
             state_dict["lr_scheduler"] = self.lr_scheduler_.state_dict()  # type: ignore[attr-defined]
 
-        epoch = kwargs.get("epoch", None)
+        epoch = kwargs.get("epoch")
         if epoch is not None:
             filename, extension = os.path.basename(filepath).split(".")
             filepath = join(