diff --git a/optimum/habana/transformers/models/gpt2/modeling_gpt2.py b/optimum/habana/transformers/models/gpt2/modeling_gpt2.py
index c790e15ff7..d5a26ed0d3 100644
--- a/optimum/habana/transformers/models/gpt2/modeling_gpt2.py
+++ b/optimum/habana/transformers/models/gpt2/modeling_gpt2.py
@@ -6,6 +6,8 @@
 from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel, logger
 from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
 
+from ....utils import get_device_name
+
 
 class GaudiGPT2Attention(torch.nn.Module):
     """
@@ -264,7 +266,16 @@ def gaudi_gpt2_block_forward(
     """
 
     residual = hidden_states
-    hidden_states = self.ln_1(hidden_states)
+
+    # TODO: remove this workaround when SynapseAI 1.13 is released
+    if not self.ln_1.training and get_device_name() == "gaudi2" and hidden_states.shape[:-1] == torch.Size([1, 1]):
+        # Change to 1,2,1600 and back to 1,1,1600
+        hidden_states = hidden_states.repeat([1, 2, 1])  # this changes the shape 1x2x1600
+        hidden_states = self.ln_1(hidden_states)
+        hidden_states = hidden_states[:, :1, :]
+    else:
+        hidden_states = self.ln_1(hidden_states)
+
     attn_outputs = self.attn(
         hidden_states,
         layer_past=layer_past,
@@ -302,7 +313,16 @@ def gaudi_gpt2_block_forward(
         outputs = outputs + cross_attn_outputs[2:]  # add cross attentions if we output attention weights
 
     residual = hidden_states
-    hidden_states = self.ln_2(hidden_states)
+
+    # TODO: remove this workaround when SynapseAI 1.13 is released
+    if not self.ln_2.training and get_device_name() == "gaudi2" and hidden_states.shape[:-1] == torch.Size([1, 1]):
+        # Change to 1,2,1600 and back to 1,1,1600
+        hidden_states = hidden_states.repeat([1, 2, 1])  # this changes the shape 1x2x1600
+        hidden_states = self.ln_2(hidden_states)
+        hidden_states = hidden_states[:, :1, :]
+    else:
+        hidden_states = self.ln_2(hidden_states)
+
     feed_forward_hidden_states = self.mlp(hidden_states)
     # residual connection
     hidden_states = residual + feed_forward_hidden_states
@@ -501,7 +521,14 @@ def custom_forward(*inputs):
                 if i == v[-1] and "cuda:" + str(k) != self.last_device:
                     hidden_states = hidden_states.to("cuda:" + str(k + 1))
 
-    hidden_states = self.ln_f(hidden_states)
+    # TODO: remove this workaround when SynapseAI 1.13 is released
+    if not self.ln_f.training and get_device_name() == "gaudi2" and hidden_states.shape[:-1] == torch.Size([1, 1]):
+        # Change to 1,2,1600 and back to 1,1,1600
+        hidden_states = hidden_states.repeat([1, 2, 1])  # this changes the shape 1x2x1600
+        hidden_states = self.ln_f(hidden_states)
+        hidden_states = hidden_states[:, :1, :]
+    else:
+        hidden_states = self.ln_f(hidden_states)
 
     hidden_states = hidden_states.view(output_shape)
     # Add last hidden state