diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
index 1a2b732e85e4..2cdb5257a944 100644
--- a/src/transformers/models/mistral/modeling_mistral.py
+++ b/src/transformers/models/mistral/modeling_mistral.py
@@ -72,11 +72,9 @@ def forward(self, hidden_states):
         variance = hidden_states.pow(2).mean(-1, keepdim=True)
         hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
         return self.weight * hidden_states.to(input_dtype)
-
     def extra_repr(self):
         return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
 
-
 class MistralRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
@@ -84,8 +82,17 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         self.dim = dim
         self.max_position_embeddings = max_position_embeddings
         self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        
+        # Init function for inv_freq
+        def init_inv_freq(device: torch.device):
+            inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+            return inv_freq
+
+        self.register_buffer("inv_freq", init_inv_freq(device), persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+        # Save buffer init callback
+        MistralModel.buf_init_callbacks.setdefault("rotary_emb.inv_freq", init_inv_freq)
 
     @torch.no_grad()
     # copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward
@@ -700,6 +707,8 @@ class MistralModel(MistralPreTrainedModel):
     Args:
         config: MistralConfig
     """
+    # A dict from buffer FQN to its init function
+    buf_init_callbacks = {}
 
     def __init__(self, config: MistralConfig):
         super().__init__(config)
@@ -953,6 +962,8 @@ def _update_causal_mask(
 
 class MistralForCausalLM(MistralPreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
+    # A dict from buffer FQN to its init function
+    buf_init_callbacks = {}
 
     def __init__(self, config):
         super().__init__(config)
@@ -962,6 +973,11 @@ def __init__(self, config):
 
         # Initialize weights and apply final processing
         self.post_init()
+        # Create buffer init callbacks by extending the one from `LlamaModel`,
+        # i.e. appending a prefix to all buffer FQNs.
+        for key, val in self.model.buf_init_callbacks.items():
+            new_key = ".".join(["pattern", key])
+            self.buf_init_callbacks[new_key] = val
 
     def get_input_embeddings(self):
         return self.model.embed_tokens
@@ -1098,10 +1114,10 @@ def prepare_inputs_for_generation(
             position_ids.masked_fill_(attention_mask == 0, 1)
             if past_key_values:
                 position_ids = position_ids[:, -input_ids.shape[1] :]
-
-                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                
+        # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                 position_ids = position_ids.clone(memory_format=torch.contiguous_format)
-
+                
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
         if inputs_embeds is not None and cache_position[0] == 0:
             model_inputs = {"inputs_embeds": inputs_embeds}