diff --git a/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py b/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py
index ae43441a1..772ef5880 100644
--- a/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py
+++ b/integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py
@@ -75,6 +75,7 @@ def __init__(
         self.local_files_only = local_files_only
         self.meta_fields_to_embed = meta_fields_to_embed or []
         self.meta_data_separator = meta_data_separator
+        self._model = None
 
     def to_dict(self) -> Dict[str, Any]:
         """
@@ -112,8 +113,8 @@ def warm_up(self):
         """
         Initializes the component.
         """
-        if not hasattr(self, "ranker"):
-            self.ranker = TextCrossEncoder(
+        if self._model is None:
+            self._model = TextCrossEncoder(
                 model_name=self.model_name,
                 cache_dir=self.cache_dir,
                 threads=self.threads,
@@ -170,14 +171,14 @@ def run(self, query: str, documents: List[Document], top_k: Optional[int] = None
             msg = f"top_k must be > 0, but got {top_k}"
             raise ValueError(msg)
 
-        if not hasattr(self, "ranker"):
+        if self._model is None:
             msg = "The ranker model has not been loaded. Please call warm_up() before running."
             raise RuntimeError(msg)
 
         fastembed_input_docs = self._prepare_fastembed_input_docs(documents)
 
         scores = list(
-            self.ranker.rerank(
+            self._model.rerank(
                 query=query,
                 documents=fastembed_input_docs,
                 batch_size=self.batch_size,
diff --git a/integrations/fastembed/tests/test_fastembed_ranker.py b/integrations/fastembed/tests/test_fastembed_ranker.py
index d2339c148..e38229c87 100644
--- a/integrations/fastembed/tests/test_fastembed_ranker.py
+++ b/integrations/fastembed/tests/test_fastembed_ranker.py
@@ -180,7 +180,7 @@ def test_run_incorrect_input_format(self):
         Test for checking incorrect input format.
         """
         ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-12-v2")
-        ranker.warm_up()
+        ranker._model = "mock_model"
 
         query = "query"
         string_input = "text"
@@ -222,7 +222,6 @@ def test_run_no_warmup(self):
 
         with pytest.raises(
             RuntimeError,
-            match=r"The ranker model has not been loaded. Please call warm_up\(\) before running.",
         ):
             ranker.run(query=query, documents=list_document)
 
@@ -231,7 +230,7 @@ def test_run_empty_document_list(self):
         Test for no error when sending no documents.
         """
         ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-12-v2")
-        ranker.warm_up()
+        ranker._model = "mock_model"
 
         query = "query"
         list_document = []
@@ -247,13 +246,13 @@ def test_embed_metadata(self):
             model_name="model_name",
             meta_fields_to_embed=["meta_field"],
         )
-        ranker.ranker = MagicMock()
+        ranker._model = MagicMock()
 
         documents = [Document(content=f"document-number {i}", meta={"meta_field": f"meta_value {i}"}) for i in range(5)]
         query = "test"
         ranker.run(query=query, documents=documents)
 
-        ranker.ranker.rerank.assert_called_once_with(
+        ranker._model.rerank.assert_called_once_with(
             query=query,
             documents=[
                 "meta_value 0\ndocument-number 0",