diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4ddabac..ddd638f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -28,14 +28,14 @@ repos:
       - id: python-use-type-annotations
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.4.1
+    rev: v2.4.3
     hooks:
       - id: codespell
         additional_dependencies: [tomli]
         #args: ["--write-changes"]
 
   - repo: https://github.com/JoC0de/pre-commit-prettier
-    rev: v3.6.2 # v3.6.2 using SHA as tags are not persistent
+    rev: v3.9.6 # v3.6.2 using SHA as tags are not persistent
     hooks:
       - id: prettier
         files: \.(json|yml|yaml|toml)
@@ -43,7 +43,7 @@ repos:
         args: ["--print-width=120"]
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.22
+    rev: 1.0.0
     hooks:
       - id: mdformat
         args: ["--number"]
@@ -54,7 +54,7 @@ repos:
         exclude: CHANGELOG.md
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.13.3
+    rev: v0.16.6
     hooks:
       - id: ruff
         args: ["--fix"]
diff --git a/README.md b/README.md
index 4a8fd3e..bb8f1a9 100644
--- a/README.md
+++ b/README.md
@@ -138,12 +138,10 @@ from tensorflow import keras
 from litmodels import save_model
 
 # Define the model
-model = keras.Sequential(
-    [
-        keras.layers.Dense(10, input_shape=(784,), name="dense_1"),
-        keras.layers.Dense(10, name="dense_2"),
-    ]
-)
+model = keras.Sequential([
+    keras.layers.Dense(10, input_shape=(784,), name="dense_1"),
+    keras.layers.Dense(10, name="dense_2"),
+])
 
 # Compile the model
 model.compile(optimizer="adam", loss="categorical_crossentropy")
@@ -157,9 +155,7 @@ Load model:
 ```python
 from litmodels import load_model
 
-model_ = load_model(
-    "lightning-ai/jirka/sample-tf-keras-model", download_dir="./my-model"
-)
+model_ = load_model("lightning-ai/jirka/sample-tf-keras-model", download_dir="./my-model")
 ```
 
 </details>
@@ -178,9 +174,7 @@ iris = datasets.load_iris()
 X, y = iris.data, iris.target
 
 # Split dataset into training and test sets
-X_train, X_test, y_train, y_test = model_selection.train_test_split(
-    X, y, test_size=0.2, random_state=42
-)
+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=42)
 
 # Train a simple SVC model
 model = svm.SVC()
@@ -196,9 +190,7 @@ Use model:
 from litmodels import load_model
 
 # Download and load the model file from cloud storage
-model = load_model(
-    name="your_org/your_team/sklearn-svm-model", download_dir="my_models"
-)
+model = load_model(name="your_org/your_team/sklearn-svm-model", download_dir="my_models")
 
 # Example: run inference with the loaded model
 sample_input = [[5.1, 3.5, 1.4, 0.2]]