diff --git a/src/transformers/models/colqwen2/modeling_colqwen2.py b/src/transformers/models/colqwen2/modeling_colqwen2.py
index df95aa5fbe53..4e3b0f08a441 100644
--- a/src/transformers/models/colqwen2/modeling_colqwen2.py
+++ b/src/transformers/models/colqwen2/modeling_colqwen2.py
@@ -27,7 +27,8 @@
 from ... import initialization as init
 from ...cache_utils import Cache
 from ...modeling_utils import PreTrainedModel
-from ...utils import ModelOutput, auto_docstring, can_return_tuple, is_torch_available
+from ...processing_utils import Unpack
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available
 from .configuration_colqwen2 import ColQwen2Config
 
 
@@ -133,12 +134,9 @@ def forward(
         labels: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         pixel_values: torch.Tensor | None = None,
         image_grid_thw: torch.LongTensor | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> ColQwen2ForRetrievalOutput:
         r"""
         image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
@@ -152,12 +150,9 @@ def forward(
             mask = arange.unsqueeze(0) < offsets.unsqueeze(1)  # (batch_size, max_len)
             pixel_values = pixel_values[mask]  # (total_valid_patches, channels, height, width)
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = kwargs.pop("output_hidden_states", None)
+        if output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
 
         # Custom data preparation to fix an issue with the gradient flow when training with multiple GPUs.
         if inputs_embeds is None:
@@ -180,9 +175,8 @@ def forward(
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
             use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            output_hidden_states=True,
+            **kwargs,
         )
 
         vlm_hidden_states = vlm_output.hidden_states if output_hidden_states else None
diff --git a/src/transformers/models/colqwen2/modular_colqwen2.py b/src/transformers/models/colqwen2/modular_colqwen2.py
index d28367a45857..69ac33d4ab06 100644
--- a/src/transformers/models/colqwen2/modular_colqwen2.py
+++ b/src/transformers/models/colqwen2/modular_colqwen2.py
@@ -19,7 +19,7 @@
 from ...image_utils import ImageInput, is_valid_image
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import ModelOutput, auto_docstring, can_return_tuple, is_torch_available, logging
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available, logging
 from ..colpali.modeling_colpali import ColPaliForRetrieval, ColPaliPreTrainedModel
 from ..colpali.processing_colpali import ColPaliProcessor
 from .configuration_colqwen2 import ColQwen2Config
@@ -274,12 +274,9 @@ def forward(
         labels: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         pixel_values: torch.Tensor | None = None,
         image_grid_thw: torch.LongTensor | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> ColQwen2ForRetrievalOutput:
         r"""
         image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
@@ -293,12 +290,9 @@ def forward(
             mask = arange.unsqueeze(0) < offsets.unsqueeze(1)  # (batch_size, max_len)
             pixel_values = pixel_values[mask]  # (total_valid_patches, channels, height, width)
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = kwargs.pop("output_hidden_states", None)
+        if output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
 
         # Custom data preparation to fix an issue with the gradient flow when training with multiple GPUs.
         if inputs_embeds is None:
@@ -321,9 +315,8 @@ def forward(
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
             use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            output_hidden_states=True,
+            **kwargs,
         )
 
         vlm_hidden_states = vlm_output.hidden_states if output_hidden_states else None