diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index c64ee7b749e876..4d85a783c8c638 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -861,7 +861,7 @@ def __init__(
                 raise ValueError(f"{device} unrecognized or not available.")
         else:
             self.device = device if device is not None else -1
-        self.torch_dtype = torch_dtype
+
         self.binary_output = binary_output
 
         # We shouldn't call `model.to()` for models loaded with accelerate
@@ -964,6 +964,13 @@ def predict(self, X):
         """
         return self(X)
 
+    @property
+    def torch_dtype(self) -> Optional["torch.dtype"]:
+        """
+        Torch dtype of the model (if it's Pytorch model), `None` otherwise.
+        """
+        return getattr(self.model, "dtype", None)
+
     @contextmanager
     def device_placement(self):
         """
diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index 5e3e15f39c10ea..13b97aff3216b5 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -199,6 +199,29 @@ def test_unbatch_attentions_hidden_states(self):
         outputs = text_classifier(["This is great !"] * 20, batch_size=32)
         self.assertEqual(len(outputs), 20)
 
+    @require_torch
+    def test_torch_dtype_property(self):
+        import torch
+
+        model_id = "hf-internal-testing/tiny-random-distilbert"
+
+        # If dtype is specified in the pipeline constructor, the property should return that type
+        pipe = pipeline(model=model_id, torch_dtype=torch.float16)
+        self.assertEqual(pipe.torch_dtype, torch.float16)
+
+        # If the underlying model changes dtype, the property should return the new type
+        pipe.model.to(torch.bfloat16)
+        self.assertEqual(pipe.torch_dtype, torch.bfloat16)
+
+        # If dtype is NOT specified in the pipeline constructor, the property should just return
+        # the dtype of the underlying model (default)
+        pipe = pipeline(model=model_id)
+        self.assertEqual(pipe.torch_dtype, torch.float32)
+
+        # If underlying model doesn't have dtype property, simply return None
+        pipe.model = None
+        self.assertIsNone(pipe.torch_dtype)
+
 
 @is_pipeline_test
 class PipelineScikitCompatTest(unittest.TestCase):