diff --git a/model2vec/model.py b/model2vec/model.py
index 324062c..a13ab46 100644
--- a/model2vec/model.py
+++ b/model2vec/model.py
@@ -476,8 +476,9 @@ def _encode_batch(self, sentences: Sequence[str], normalize: bool) -> np.ndarray
                     out[i] = emb.mean(axis=0)
 
         if normalize:
-            norm = np.linalg.norm(out, axis=1, keepdims=True) + 1e-32
-            np.divide(out, norm, out=out)
+            out32 = out.astype(np.float32)
+            norm = np.linalg.norm(out32, axis=1, keepdims=True) + 1e-32
+            return (out32 / norm).astype(out.dtype)
 
         return out
 
diff --git a/tests/test_model.py b/tests/test_model.py
index df9f25e..b7a062a 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -53,6 +53,18 @@ def test_encode_single_sentence_empty(
     assert np.all(encoded == 0)
 
 
+def test_encode_single_sentence_empty_float16(
+    mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str]
+) -> None:
+    """Test encoding of a single empty sentence with float16 embeddings."""
+    model = StaticModel(vectors=mock_vectors.astype(np.float16), tokenizer=mock_tokenizer, config=mock_config)
+    model.normalize = True
+    encoded = model.encode("")
+    assert not np.isnan(encoded).any()
+    assert np.all(encoded == 0)
+    assert encoded.dtype == model.embedding.dtype
+
+
 def test_encode_multiple_sentences(
     mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str]
 ) -> None: