diff --git a/optimum/exporters/openvino/model_patcher.py b/optimum/exporters/openvino/model_patcher.py
index 825eaac48..f0bc25e36 100644
--- a/optimum/exporters/openvino/model_patcher.py
+++ b/optimum/exporters/openvino/model_patcher.py
@@ -2501,6 +2501,40 @@ def __enter__(self):
                 _reinitialize_cos_sin_cached_fp32(layer.self_attn.rotary_emb)
 
 
+# Adapted from https://github.com/huggingface/transformers/blob/31f9a289a6207be6cae746e009d8e0db523be203/src/transformers/models/falcon/modeling_falcon.py#L1138
+def _falcon_prepare_4d_causal_attention_mask_with_cache_position(
+    attention_mask: torch.Tensor,
+    sequence_length: int,
+    target_length: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    cache_position: torch.Tensor,
+    batch_size: int,
+    **kwargs,
+):
+    if attention_mask is not None and attention_mask.dim() == 4:
+        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+        causal_mask = attention_mask
+    else:
+        # different from original: allow to provide min_dtype as parameter
+        min_dtype = torch.finfo(dtype).min if "min_dtype" not in kwargs else kwargs["min_dtype"]
+        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                padding_mask, min_dtype
+            )
+
+    return causal_mask
+
+
 def _falcon_update_causal_mask(
     self,
     attention_mask: torch.Tensor,
@@ -2520,13 +2554,6 @@ def _falcon_update_causal_mask(
     # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
     # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
 
-    if hasattr(self, "_prepare_4d_causal_attention_mask_with_cache_position"):
-        _prepare_4d_causal_attention_mask_with_cache_position = (
-            self._prepare_4d_causal_attention_mask_with_cache_position
-        )
-    else:
-        from transformers.models.falcon.modeling_falcon import _prepare_4d_causal_attention_mask_with_cache_position
-
     if self.config._attn_implementation == "flash_attention_2":
         if attention_mask is not None and 0.0 in attention_mask:
             return attention_mask
@@ -2568,7 +2595,7 @@ def _falcon_update_causal_mask(
         )
 
     # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-    causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
+    causal_mask = _falcon_prepare_4d_causal_attention_mask_with_cache_position(
         attention_mask,
         sequence_length=sequence_length,
         target_length=target_length,