diff --git a/src/transformers/models/mixtral/modeling_mixtral.py b/src/transformers/models/mixtral/modeling_mixtral.py
index b6b5883f4a77..b1d995b27474 100644
--- a/src/transformers/models/mixtral/modeling_mixtral.py
+++ b/src/transformers/models/mixtral/modeling_mixtral.py
@@ -30,30 +30,33 @@
 import torch.nn.functional as F
 from torch import nn
 
-from transformers.utils.generic import check_model_inputs
-
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub
 from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
-from ...modeling_layers import (
-    GenericForQuestionAnswering,
-    GenericForSequenceClassification,
-    GenericForTokenClassification,
-    GradientCheckpointingLayer,
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+    BaseModelOutputWithPast,
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
 )
-from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
 from ...utils.deprecation import deprecate_kwarg
 from ...utils.generic import OutputRecorder
 from .configuration_mixtral import MixtralConfig
 
 
+logger = logging.get_logger(__name__)
+
+
 class MixtralBlockSparseTop2MLP(nn.Module):
     def __init__(self, config: MixtralConfig):
         super().__init__()
@@ -115,26 +118,46 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         routing_weights = routing_weights.to(hidden_states.dtype)
 
         final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+            (batch_size * sequence_length, hidden_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
         )
 
         # One hot encode the selected experts to create an expert mask
         # this will be used to easily index which expert is going to be sollicitated
         expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
 
-        expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
-        for expert_idx in expert_hit:
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        # Separate paths for training (with .nonzero()) and inference (without .nonzero())
+        if self.training:
+            # Training path: use .nonzero() for efficiency (skip non-selected experts)
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+            for expert_idx in expert_hit:
+                expert_layer = self.experts[expert_idx]
+                idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+                # Index the correct hidden states and compute the expert hidden state for
+                # the current expert. We need to make sure to multiply the output hidden
+                # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+                current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+                current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+                # However `index_add_` only support torch tensors for indexing so we'll use
+                # the `top_x` tensor here.
+                final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        else:
+            # Inference path: loop over all experts for torch.export compatibility
+            for expert_idx in range(self.num_experts):
+                expert_layer = self.experts[expert_idx]
+                idx, top_x = torch.where(expert_mask[expert_idx])
+
+                # Index the correct hidden states and compute the expert hidden state for
+                # the current expert. We need to make sure to multiply the output hidden
+                # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+                current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+                current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+                # However `index_add_` only support torch tensors for indexing so we'll use
+                # the `top_x` tensor here.
+                final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
         final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
         return final_hidden_states, router_logits
 
@@ -214,7 +237,7 @@ def eager_attention_forward(
     attention_mask: Optional[torch.Tensor],
     scaling: float,
     dropout: float = 0.0,
-    **kwargs: Unpack[TransformersKwargs],
+    **kwargs,
 ):
     key_states = repeat_kv(key, module.num_key_value_groups)
     value_states = repeat_kv(value, module.num_key_value_groups)
@@ -249,16 +272,15 @@ def __init__(self, config: MixtralConfig, layer_idx: int):
         self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
         self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
 
-    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
     def forward(
         self,
         hidden_states: torch.Tensor,
         position_embeddings: tuple[torch.Tensor, torch.Tensor],
         attention_mask: Optional[torch.Tensor],
-        past_key_values: Optional[Cache] = None,
+        past_key_value: Optional[Cache] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
@@ -269,10 +291,10 @@ def forward(
         cos, sin = position_embeddings
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
-        if past_key_values is not None:
+        if past_key_value is not None:
             # sin and cos are specific to RoPE models; cache_position needed for the static cache
             cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
 
         attention_interface: Callable = eager_attention_forward
         if self.config._attn_implementation != "eager":
@@ -343,12 +365,10 @@ def forward(
 
 
 class MixtralRotaryEmbedding(nn.Module):
-    inv_freq: torch.Tensor  # fix linting for `register_buffer`
-
     def __init__(self, config: MixtralConfig, device=None):
         super().__init__()
         # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
             self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
         else:
             self.rope_type = "default"
@@ -380,22 +400,39 @@ def forward(self, x, position_ids):
 
 @auto_docstring
 class MixtralPreTrainedModel(PreTrainedModel):
-    config: MixtralConfig
+    config_class = MixtralConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
     _no_split_modules = ["MixtralDecoderLayer"]
     _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn = True
+    _supports_flash_attn_3 = True
+    _supports_flash_attn_2 = True
     _supports_sdpa = True
     _supports_flex_attn = True
-    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
     _supports_attention_backend = True
+    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
     _can_record_outputs = {
         "router_logits": OutputRecorder(MixtralSparseMoeBlock, index=1),
         "hidden_states": MixtralDecoderLayer,
         "attentions": MixtralAttention,
     }
 
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, MixtralRMSNorm):
+            module.weight.data.fill_(1.0)
+
 
 @auto_docstring
 class MixtralModel(MixtralPreTrainedModel):
@@ -415,7 +452,13 @@ def __init__(self, config: MixtralConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
-    @check_model_inputs
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -440,7 +483,9 @@ def forward(
         if cache_position is None:
             past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
             cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
             )
         if position_ids is None:
             position_ids = cache_position.unsqueeze(0)
@@ -580,6 +625,18 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
     def set_decoder(self, decoder):
         self.model = decoder
 
@@ -673,16 +730,238 @@ def forward(
         )
 
 
-class MixtralForSequenceClassification(GenericForSequenceClassification, MixtralPreTrainedModel):
-    pass
+@auto_docstring(
+    custom_intro="""
+    The Mixtral Model transformer with a sequence classification head on top (linear layer).
+
+    [`MixtralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """
+)
+class MixtralForSequenceClassification(MixtralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MixtralModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> SequenceClassifierOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        transformer_outputs: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        hidden_states = transformer_outputs.last_hidden_state
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+        else:
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+            )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@auto_docstring
+class MixtralForTokenClassification(MixtralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MixtralModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) -> TokenClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+        sequence_output = outputs.last_hidden_state
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.config)
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
 
 
-class MixtralForTokenClassification(GenericForTokenClassification, MixtralPreTrainedModel):
-    pass
+@auto_docstring
+class MixtralForQuestionAnswering(MixtralPreTrainedModel):
+    base_model_prefix = "model"
 
+    def __init__(self, config):
+        super().__init__(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+        self.model = MixtralModel(config)  # diff with Llama: transformer->model
+
+        # Initialize weights and apply final processing
+        self.post_init()
 
-class MixtralForQuestionAnswering(GenericForQuestionAnswering, MixtralPreTrainedModel):
-    pass
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs,
+    ) -> QuestionAnsweringModelOutput:
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        sequence_output = outputs.last_hidden_state
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+
+        return QuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
 
 
 __all__ = [
diff --git a/src/transformers/models/mixtral/modular_mixtral.py b/src/transformers/models/mixtral/modular_mixtral.py
index ffcf8224353f..ba7d957a60c1 100644
--- a/src/transformers/models/mixtral/modular_mixtral.py
+++ b/src/transformers/models/mixtral/modular_mixtral.py
@@ -86,7 +86,9 @@ def load_balancing_loss_func(
 
     if isinstance(gate_logits, tuple):
         compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+        concatenated_gate_logits = torch.cat(
+            [layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0
+        )
 
     routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
 
@@ -102,20 +104,24 @@ def load_balancing_loss_func(
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
         batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (
+            batch_size * sequence_length
+        )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
             attention_mask[None, :, :, None, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .expand(
+                (num_hidden_layers, batch_size, sequence_length, top_k, num_experts)
+            )
             .reshape(-1, top_k, num_experts)
             .to(compute_device)
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
-        )
+        tokens_per_expert = torch.sum(
+            expert_mask.float() * expert_attention_mask, dim=0
+        ) / torch.sum(expert_attention_mask, dim=0)
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
         router_per_expert_attention_mask = (
@@ -126,9 +132,9 @@ def load_balancing_loss_func(
         )
 
         # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
-            router_per_expert_attention_mask, dim=0
-        )
+        router_prob_per_expert = torch.sum(
+            routing_weights * router_per_expert_attention_mask, dim=0
+        ) / torch.sum(router_per_expert_attention_mask, dim=0)
 
     overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
     return overall_loss * num_experts
@@ -147,7 +153,9 @@ def __init__(self, config: MixtralConfig):
         self.act_fn = ACT2FN[config.hidden_act]
 
     def forward(self, hidden_states):
-        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(
+            hidden_states
+        )
         current_hidden_states = self.w2(current_hidden_states)
         return current_hidden_states
 
@@ -174,7 +182,9 @@ def __init__(self, config):
         # gating
         self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
 
-        self.experts = nn.ModuleList([MixtralBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
+        self.experts = nn.ModuleList(
+            [MixtralBlockSparseTop2MLP(config) for _ in range(self.num_experts)]
+        )
 
         # Jitter parameters
         self.jitter_noise = config.router_jitter_noise
@@ -183,39 +193,75 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         """ """
         batch_size, sequence_length, hidden_dim = hidden_states.shape
         if self.training and self.jitter_noise > 0:
-            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+            hidden_states *= torch.empty_like(hidden_states).uniform_(
+                1.0 - self.jitter_noise, 1.0 + self.jitter_noise
+            )
         hidden_states = hidden_states.view(-1, hidden_dim)
         # router_logits: (batch * sequence_length, n_experts)
         router_logits = self.gate(hidden_states)
 
         routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        routing_weights, selected_experts = torch.topk(
+            routing_weights, self.top_k, dim=-1
+        )
         routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
         # we cast back to the input dtype
         routing_weights = routing_weights.to(hidden_states.dtype)
 
         final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+            (batch_size * sequence_length, hidden_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
         )
 
         # One hot encode the selected experts to create an expert mask
         # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
-
-        expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
-        for expert_idx in expert_hit:
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        expert_mask = torch.nn.functional.one_hot(
+            selected_experts, num_classes=self.num_experts
+        ).permute(2, 1, 0)
+
+        # Separate paths for training (with .nonzero()) and inference (without .nonzero())
+        if self.training:
+            # Training path: use .nonzero() for efficiency (skip non-selected experts)
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+            for expert_idx in expert_hit:
+                expert_layer = self.experts[expert_idx]
+                idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+                # Index the correct hidden states and compute the expert hidden state for
+                # the current expert. We need to make sure to multiply the output hidden
+                # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+                current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+                current_hidden_states = (
+                    expert_layer(current_state) * routing_weights[top_x, idx, None]
+                )
+
+                # However `index_add_` only support torch tensors for indexing so we'll use
+                # the `top_x` tensor here.
+                final_hidden_states.index_add_(
+                    0, top_x, current_hidden_states.to(hidden_states.dtype)
+                )
+        else:
+            # Inference path: loop over all experts for torch.export compatibility
+            for expert_idx in range(self.num_experts):
+                expert_layer = self.experts[expert_idx]
+                idx, top_x = torch.where(expert_mask[expert_idx])
+
+                # Index the correct hidden states and compute the expert hidden state for
+                # the current expert. We need to make sure to multiply the output hidden
+                # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+                current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+                current_hidden_states = (
+                    expert_layer(current_state) * routing_weights[top_x, idx, None]
+                )
+
+                # However `index_add_` only support torch tensors for indexing so we'll use
+                # the `top_x` tensor here.
+                final_hidden_states.index_add_(
+                    0, top_x, current_hidden_states.to(hidden_states.dtype)
+                )
+        final_hidden_states = final_hidden_states.reshape(
+            batch_size, sequence_length, hidden_dim
+        )
         return final_hidden_states, router_logits
 
 
@@ -235,8 +281,12 @@ def __init__(self, config: MixtralConfig, layer_idx: int):
         self.self_attn = MixtralAttention(config, layer_idx)
 
         self.block_sparse_moe = MixtralSparseMoeBlock(config)
-        self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_layernorm = MixtralRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = MixtralRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
 
     @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
     def forward(
@@ -300,7 +350,9 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> MoeModelOutputWithPast:
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            )
 
         if use_cache and past_key_values is None:
             past_key_values = DynamicCache(config=self.config)
@@ -309,14 +361,22 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            past_seen_tokens = (
+                past_key_values.get_seq_length() if past_key_values is not None else 0
+            )
             cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
             )
         if position_ids is None:
             position_ids = cache_position.unsqueeze(0)
 
-        mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+        mask_function = (
+            create_causal_mask
+            if self.config.sliding_window is None
+            else create_sliding_window_causal_mask
+        )
         causal_mask = mask_function(
             config=self.config,
             input_embeds=inputs_embeds,
@@ -399,7 +459,9 @@ def forward(
         ```"""
 
         output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+            output_router_logits
+            if output_router_logits is not None
+            else self.config.output_router_logits
         )
 
         # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
@@ -417,7 +479,11 @@ def forward(
 
         hidden_states = outputs.last_hidden_state
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
         logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
@@ -433,7 +499,9 @@ def forward(
                 attention_mask,
             )
             if labels is not None:
-                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+                loss += self.router_aux_loss_coef * aux_loss.to(
+                    loss.device
+                )  # make sure to reside in the same device
 
         return MoeCausalLMOutputWithPast(
             loss=loss,
diff --git a/tests/models/mixtral/test_mixtral_torch_export.py b/tests/models/mixtral/test_mixtral_torch_export.py
new file mode 100644
index 000000000000..0aa39baa61d8
--- /dev/null
+++ b/tests/models/mixtral/test_mixtral_torch_export.py
@@ -0,0 +1,151 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing torch.export compatibility for Mixtral models."""
+
+import unittest
+
+import torch
+import torch.export as te
+
+from transformers import MixtralConfig
+from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
+from transformers.testing_utils import require_torch
+
+
+@require_torch
+class MixtralTorchExportTest(unittest.TestCase):
+    """Test torch.export compatibility for Mixtral MoE components."""
+
+    def setUp(self):
+        """Set up test configuration."""
+        self.config = MixtralConfig(
+            hidden_size=128,
+            intermediate_size=256,
+            num_local_experts=8,
+            num_experts_per_tok=2,
+            router_jitter_noise=0.0,
+        )
+
+    def test_moe_block_torch_export(self):
+        """Test that MixtralSparseMoeBlock can be exported with torch.export in inference mode."""
+        # Create MoE block
+        moe_block = MixtralSparseMoeBlock(self.config)
+        moe_block.eval()  # Set to eval mode for inference path
+
+        # Move to meta device for export testing
+        moe_block = moe_block.to("meta")
+
+        # Create test input
+        batch_size, seq_len = 2, 8
+        hidden_states = torch.randn(
+            batch_size, seq_len, self.config.hidden_size, device="meta"
+        )
+
+        # Test torch.export - should not raise GuardOnDataDependentSymNode error
+        try:
+            exported_program = te.export(
+                moe_block, args=(hidden_states,), kwargs={}, strict=False
+            )
+            # If export succeeds, the test passes
+            self.assertIsNotNone(exported_program)
+        except Exception as e:
+            # Check if it's the specific error we're trying to avoid
+            error_msg = str(e)
+            if (
+                "GuardOnDataDependentSymNode" in error_msg
+                or "nonzero" in error_msg.lower()
+            ):
+                self.fail(
+                    f"torch.export failed with data-dependent operation error: {error_msg}\n"
+                    "This suggests the inference path has data-dependent operations that need to be removed."
+                )
+            else:
+                # Re-raise other unexpected errors
+                raise
+
+    def test_moe_block_functionality(self):
+        """Test that MoE block maintains correct functionality after the fix."""
+        # Create MoE block
+        moe_block = MixtralSparseMoeBlock(self.config)
+        moe_block.eval()
+
+        # Create test input
+        batch_size, seq_len = 2, 4
+        hidden_states = torch.randn(batch_size, seq_len, self.config.hidden_size)
+
+        # Forward pass
+        with torch.no_grad():
+            output, router_logits = moe_block(hidden_states)
+
+        # Verify output shapes
+        self.assertEqual(output.shape, hidden_states.shape)
+        self.assertEqual(
+            router_logits.shape, (batch_size * seq_len, self.config.num_local_experts)
+        )
+
+        # Verify that outputs are not all zeros (computation happened)
+        self.assertFalse(torch.allclose(output, torch.zeros_like(output)))
+
+        # Test with different input to ensure different outputs
+        hidden_states2 = torch.randn(batch_size, seq_len, self.config.hidden_size)
+        with torch.no_grad():
+            output2, _ = moe_block(hidden_states2)
+
+        # Outputs should be different for different inputs
+        self.assertFalse(torch.allclose(output, output2))
+
+    def test_moe_block_export_with_different_configs(self):
+        """Test torch.export with various expert configurations."""
+        test_configs = [
+            # (num_experts, top_k)
+            (4, 2),
+            (8, 2),
+            (16, 2),
+            (8, 4),
+        ]
+
+        for num_experts, top_k in test_configs:
+            with self.subTest(num_experts=num_experts, top_k=top_k):
+                config = MixtralConfig(
+                    hidden_size=64,
+                    intermediate_size=128,
+                    num_local_experts=num_experts,
+                    num_experts_per_tok=top_k,
+                    router_jitter_noise=0.0,
+                )
+
+                moe_block = MixtralSparseMoeBlock(config)
+                moe_block.eval()
+                moe_block = moe_block.to("meta")
+
+                hidden_states = torch.randn(1, 4, config.hidden_size, device="meta")
+
+                # Should export without errors
+                try:
+                    exported_program = te.export(
+                        moe_block, args=(hidden_states,), kwargs={}, strict=False
+                    )
+                    self.assertIsNotNone(exported_program)
+                except Exception as e:
+                    if "GuardOnDataDependentSymNode" in str(e):
+                        self.fail(
+                            f"Export failed for config ({num_experts}, {top_k}): {e}"
+                        )
+                    else:
+                        raise
+
+
+if __name__ == "__main__":
+    unittest.main()