diff --git a/src/transformers/models/modernbert/modeling_modernbert.py b/src/transformers/models/modernbert/modeling_modernbert.py
index 237fba6f645fa5..00d529824f8d16 100644
--- a/src/transformers/models/modernbert/modeling_modernbert.py
+++ b/src/transformers/models/modernbert/modeling_modernbert.py
@@ -307,7 +307,7 @@ def eager_attention_forward(
     dim: int,
     output_attentions: Optional[bool] = False,
     **_kwargs,
-) -> Tuple[torch.Tensor, torch.Tensor] | Tuple[torch.Tensor]:
+) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
     # qkv: [batch_size, seqlen, 3, nheads, headdim]
     cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
     query, key, value = qkv.transpose(3, 1).unbind(dim=2)
@@ -1125,6 +1125,12 @@ def __init__(self, config: ModernBertConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def get_input_embeddings(self):
+        return self.model.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings.tok_embeddings = value
+
     @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
@@ -1236,6 +1242,12 @@ def __init__(self, config: ModernBertConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def get_input_embeddings(self):
+        return self.model.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings.tok_embeddings = value
+
     @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
diff --git a/src/transformers/models/modernbert/modular_modernbert.py b/src/transformers/models/modernbert/modular_modernbert.py
index dac356146f3015..fb65d1d1eae3d9 100644
--- a/src/transformers/models/modernbert/modular_modernbert.py
+++ b/src/transformers/models/modernbert/modular_modernbert.py
@@ -532,7 +532,7 @@ def eager_attention_forward(
     dim: int,
     output_attentions: Optional[bool] = False,
     **_kwargs,
-) -> Tuple[torch.Tensor, torch.Tensor] | Tuple[torch.Tensor]:
+) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
     # qkv: [batch_size, seqlen, 3, nheads, headdim]
     cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
     query, key, value = qkv.transpose(3, 1).unbind(dim=2)
@@ -1278,6 +1278,12 @@ def __init__(self, config: ModernBertConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def get_input_embeddings(self):
+        return self.model.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings.tok_embeddings = value
+
     @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,
@@ -1389,6 +1395,12 @@ def __init__(self, config: ModernBertConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def get_input_embeddings(self):
+        return self.model.embeddings.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings.tok_embeddings = value
+
     @add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC,