diff --git a/model2vec/train/README.md b/model2vec/train/README.md
index 2d7aad2b..e48acbb2 100644
--- a/model2vec/train/README.md
+++ b/model2vec/train/README.md
@@ -22,7 +22,7 @@ from model2vec.train import StaticModelForClassification
 
 # From a distilled model
 distilled_model = distill("baai/bge-base-en-v1.5")
-classifier = StaticModelForClassification.from_static_model(distilled_model)
+classifier = StaticModelForClassification.from_static_model(model=distilled_model)
 
 # From a pre-trained model: potion is the default
 classifier = StaticModelForClassification.from_pretrained(model_name="minishlab/potion-base-32m")
diff --git a/model2vec/train/base.py b/model2vec/train/base.py
index db1d5394..60749fe0 100644
--- a/model2vec/train/base.py
+++ b/model2vec/train/base.py
@@ -45,14 +45,14 @@ def construct_head(self) -> nn.Sequential:
 
     @classmethod
     def from_pretrained(
-        cls: type[ModelType], out_dim: int = 2, model_name: str = "minishlab/potion-base-32m", **kwargs: Any
+        cls: type[ModelType], *, out_dim: int = 2, model_name: str = "minishlab/potion-base-32m", **kwargs: Any
     ) -> ModelType:
         """Load the model from a pretrained model2vec model."""
         model = StaticModel.from_pretrained(model_name)
-        return cls.from_static_model(model, out_dim, **kwargs)
+        return cls.from_static_model(model=model, out_dim=out_dim, **kwargs)
 
     @classmethod
-    def from_static_model(cls: type[ModelType], model: StaticModel, out_dim: int = 2, **kwargs: Any) -> ModelType:
+    def from_static_model(cls: type[ModelType], *, model: StaticModel, out_dim: int = 2, **kwargs: Any) -> ModelType:
         """Load the model from a static model."""
         model.embedding = np.nan_to_num(model.embedding)
         embeddings_converted = torch.from_numpy(model.embedding)
diff --git a/tests/test_trainable.py b/tests/test_trainable.py
index 9add8b25..ea0e354d 100644
--- a/tests/test_trainable.py
+++ b/tests/test_trainable.py
@@ -41,7 +41,7 @@ def test_init_base_class(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) ->
 def test_init_base_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None:
     """Test initializion from a static model."""
     model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer)
-    s = FinetunableStaticModel.from_static_model(model)
+    s = FinetunableStaticModel.from_static_model(model=model)
     assert s.vectors.shape == mock_vectors.shape
     assert s.w.shape[0] == mock_vectors.shape[0]
 
@@ -55,7 +55,7 @@ def test_init_base_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenize
 def test_init_classifier_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None:
     """Test initializion from a static model."""
     model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer)
-    s = StaticModelForClassification.from_static_model(model)
+    s = StaticModelForClassification.from_static_model(model=model)
     assert s.vectors.shape == mock_vectors.shape
     assert s.w.shape[0] == mock_vectors.shape[0]