diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 110ae09a388704..c312b9b94351d2 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -805,6 +805,16 @@ class Mamba2CausalLMOutput(ModelOutput):
             more detail.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
+            If `cache_params` is passed, `cache_position` should also be passed.
+        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
 """
 
 
diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
index 321d3dc0daf378..3b0fb75a4cb3ba 100644
--- a/src/transformers/models/mistral/modeling_mistral.py
+++ b/src/transformers/models/mistral/modeling_mistral.py
@@ -619,7 +619,7 @@ def _init_weights(self, module):
             Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
             `past_key_values`).
 
             If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
@@ -666,6 +666,10 @@ def _init_weights(self, module):
             more detail.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices indicating the position of the input sequence tokens in the sequence. Unlike `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
 """