diff --git a/turbo_alignment/dataset/multimodal/collators.py b/turbo_alignment/dataset/multimodal/collators.py
index 5197124..3dd7967 100644
--- a/turbo_alignment/dataset/multimodal/collators.py
+++ b/turbo_alignment/dataset/multimodal/collators.py
@@ -10,7 +10,7 @@ def torch_call(self, features):
         if 'modality_inputs' in features[0].keys():
             # print([feature['modality_inputs'] for feature in features])
             modality_inputs = torch.stack(
-                [torch.stack(feature['modality_inputs']) for feature in features]
+                [torch.stack(feature['modality_inputs']).contiguous() for feature in features]
             ).contiguous()
         else:
             modality_inputs = [None for _ in features]
diff --git a/turbo_alignment/dataset/multimodal/multimodal.py b/turbo_alignment/dataset/multimodal/multimodal.py
index c100d39..ccbb65f 100644
--- a/turbo_alignment/dataset/multimodal/multimodal.py
+++ b/turbo_alignment/dataset/multimodal/multimodal.py
@@ -194,7 +194,7 @@ def _read_modalities(self, record):
             for msg in modality_messages:
                 reader = self._modality_readers[msg.type]
                 # modality_encodings.append((msg.type, reader.read(msg.content)))
-                modality_encodings.append(reader.read(msg.content))
+                modality_encodings.append(reader.read(msg.content).contiguous())
         except (OSError, RuntimeError, KeyError):
             return None
 
diff --git a/turbo_alignment/modeling/multimodal/lm/projection.py b/turbo_alignment/modeling/multimodal/lm/projection.py
index 653b285..33193b7 100644
--- a/turbo_alignment/modeling/multimodal/lm/projection.py
+++ b/turbo_alignment/modeling/multimodal/lm/projection.py
@@ -83,7 +83,7 @@ def convert_inputs_to_embeds(
             for index, modality_object in enumerate(sample_modality_inputs):
                 # modality, inputs = modality_object
                 # grouped_modality_encoder_inputs[modality].append((index, inputs))
-                inputs = modality_object
+                inputs = modality_object.contiguous()
                 grouped_modality_encoder_inputs.append((index, inputs))
 
             sorted_modality_embeddings: torch.Tensor = torch.full(
@@ -126,12 +126,12 @@ def convert_inputs_to_embeds(
     def forward(
         self,
         input_ids: torch.LongTensor,
-        modality_inputs: list[list[tuple[Modality, torch.Tensor]]],
+        modality_inputs,
         attention_mask: torch.LongTensor,
         modality_tokens_mask: torch.Tensor,
         labels: torch.LongTensor | None = None,
     ) -> ModelOutput:
-        multimodal_lm_input_embeds = self.convert_inputs_to_embeds(input_ids, modality_inputs, modality_tokens_mask)
+        multimodal_lm_input_embeds = self.convert_inputs_to_embeds(input_ids, modality_inputs.contiguous(), modality_tokens_mask)
         return self.language_model(
             inputs_embeds=multimodal_lm_input_embeds, labels=labels, attention_mask=attention_mask
         )
diff --git a/turbo_alignment/pipelines/inference/multimodal.py b/turbo_alignment/pipelines/inference/multimodal.py
index addaf11..b8701ce 100755
--- a/turbo_alignment/pipelines/inference/multimodal.py
+++ b/turbo_alignment/pipelines/inference/multimodal.py
@@ -40,6 +40,7 @@ def _load_model(
             peft=True,
         )
         model.modality_adapters.load_state_dict(torch.load(inference_settings.model_settings.projections_path))
+        for param in model.parameters(): param.data = param.data.contiguous()
 
         return model