diff --git a/src/transformers/models/auto/auto_factory.py b/src/transformers/models/auto/auto_factory.py
index 17cc7f9579..aad113d454 100644
--- a/src/transformers/models/auto/auto_factory.py
+++ b/src/transformers/models/auto/auto_factory.py
@@ -435,19 +435,24 @@ class _BaseAutoModelClass:
         ]
         hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
         if not isinstance(config, PretrainedConfig):
-            kwargs_copy = copy.deepcopy(kwargs)
+            kwargs_orig = copy.deepcopy(kwargs)
             # ensure not to pollute the config object with torch_dtype="auto" - since it's
             # meaningless in the context of the config object - torch.dtype values are acceptable
-            if kwargs_copy.get("torch_dtype", None) == "auto":
-                _ = kwargs_copy.pop("torch_dtype")
+            if kwargs.get("torch_dtype", None) == "auto":
+                _ = kwargs.pop("torch_dtype")
 
             config, kwargs = AutoConfig.from_pretrained(
                 pretrained_model_name_or_path,
                 return_unused_kwargs=True,
                 trust_remote_code=trust_remote_code,
                 **hub_kwargs,
-                **kwargs_copy,
+                **kwargs,
             )
+
+            # if torch_dtype=auto was passed here, ensure to pass it on
+            if kwargs_orig.get("torch_dtype", None) == "auto":
+                kwargs["torch_dtype"] = "auto"
+
         if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
             if not trust_remote_code:
                 raise ValueError(
diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py
index 43e06e9067..e6e7968ae5 100755
--- a/tests/test_modeling_common.py
+++ b/tests/test_modeling_common.py
@@ -2920,6 +2920,10 @@ class ModelUtilsTest(TestCasePlus):
         model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto")
         self.assertEqual(model.dtype, torch.float16)
 
+        # 3. now retest that AutoModel behaves the same wrt torch_dtype="auto" as T5ForConditionalGeneration
+        model = AutoModel.from_pretrained(model_path, torch_dtype="auto")
+        self.assertEqual(model.dtype, torch.float16)
+
         # test fp16 save_pretrained, loaded with the explicit fp16
         model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16)
         self.assertEqual(model.dtype, torch.float16)