diff --git a/tests/models/multimodal/test_mapping.py b/tests/models/multimodal/test_mapping.py
index 1b7e530f3..8d4ccaf4e 100644
--- a/tests/models/multimodal/test_mapping.py
+++ b/tests/models/multimodal/test_mapping.py
@@ -31,12 +31,6 @@ def create_dummy_model(repo: str, model_arch: str) -> PreTrainedModel:
     config = AutoConfig.from_pretrained(repo)
     with torch.device("meta"):
         model = model_cls._from_config(config)
-    # TODO(hmellor): Remove this once Transformers has fixed tied weights on meta device
-    # https://github.com/huggingface/transformers/issues/43522
-    if getattr(config.get_text_config(), "tie_word_embeddings", False) or getattr(
-        config, "tie_word_embeddings", False
-    ):
-        model.tie_weights()
     return model
 
 
@@ -103,6 +97,15 @@ def test_hf_model_weights_mapper(model_arch: str):
     # Some checkpoints may have buffers, we ignore them for this test
     ref_weight_names -= buffer_names
 
+    # Some checkpoints include tied weights (e.g. lm_head tied to embed_tokens) in the
+    # safetensors file. In Transformers v5, named_parameters() will not include them
+    # after they are tied in the model, so the mapper will not be able to map them.
+    # We exclude them from the reference weight names for this test.
+    if isinstance(tied := getattr(hf_dummy_model, "_tied_weights_keys", None), dict):
+        mapped_tied_weights = mapper.apply((k, None) for k in tied)
+        tied_weight_names = set(map(lambda x: x[0], mapped_tied_weights))
+        ref_weight_names -= tied_weight_names
+
     weights_missing = ref_weight_names - weight_names
     weights_unmapped = weight_names - ref_weight_names
     assert not weights_missing and not weights_unmapped, (