diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 2fe92d3e3ed64b..c1aa338a7d8f2f 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -34,13 +34,6 @@
 )
 from ..integrations.deepspeed import is_deepspeed_zero3_enabled
 from ..modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
-from ..models.auto import (
-    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
-    MODEL_FOR_CAUSAL_LM_MAPPING,
-    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
-    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
-    MODEL_FOR_VISION_2_SEQ_MAPPING,
-)
 from ..pytorch_utils import isin_mps_friendly
 from ..tokenization_utils import ExtensionsTrie
 from ..utils import (
@@ -1117,26 +1110,21 @@ def _validate_model_class(self):
         Confirms that the model class is compatible with generation. If not, raises an exception that points to the
         right class to use.
         """
+        # TODO(joao): remove this function in v4.50, i.e. when we remove the inheritance of `GenerationMixin` from
+        # `PreTrainedModel`. With that inheritance removed, all model classes inheriting from `GenerationMixin` can
+        # safely call `GenerationMixin.generate`
         if not is_torchdynamo_compiling() and not self.can_generate():
-            generate_compatible_mappings = [
-                MODEL_FOR_CAUSAL_LM_MAPPING,
-                MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
-                MODEL_FOR_VISION_2_SEQ_MAPPING,
-                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
-                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+            terminations_with_generation_support = [
+                "ForCausalLM",
+                "ForConditionalGeneration",
+                "ForSpeechSeq2Seq",
+                "ForVision2Seq",
             ]
-            generate_compatible_classes = set()
-            for model_mapping in generate_compatible_mappings:
-                supported_models = model_mapping.get(type(self.config), default=None)
-                if supported_models is not None:
-                    generate_compatible_classes.add(supported_models.__name__)
-            exception_message = (
+            raise TypeError(
                 f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
-                "it doesn't have a language model head."
+                "it doesn't have a language model head. Classes that support generation often end in one of these "
+                f"names: {terminations_with_generation_support}."
             )
-            if generate_compatible_classes:
-                exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
-            raise TypeError(exception_message)
 
     def _validate_assistant(self, assistant_model):
         if assistant_model is None:
diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index d4069766636053..6fff23f6b6df13 100755
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -212,7 +212,7 @@ def _skip_init(*args, **kwargs):
                 setattr(torch.nn.init, name, init_func)
 
 
-def get_parameter_device(parameter: Union[nn.Module, GenerationMixin, "ModuleUtilsMixin"]):
+def get_parameter_device(parameter: Union[nn.Module, "ModuleUtilsMixin"]):
     try:
         return next(parameter.parameters()).device
     except StopIteration:
@@ -227,7 +227,7 @@ def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
         return first_tuple[1].device
 
 
-def get_first_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "ModuleUtilsMixin"]):
+def get_first_parameter_dtype(parameter: Union[nn.Module, "ModuleUtilsMixin"]):
     """
     Returns the first parameter dtype (can be non-floating) or asserts if none were found.
     """
@@ -245,7 +245,7 @@ def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
         return first_tuple[1].dtype
 
 
-def get_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "ModuleUtilsMixin"]):
+def get_parameter_dtype(parameter: Union[nn.Module, "ModuleUtilsMixin"]):
     """
     Returns the first found floating dtype in parameters if there is one, otherwise returns the last dtype it found.
     """
@@ -1309,6 +1309,7 @@ def floating_point_ops(
         return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings)
 
 
+# TODO (joao): remove `GenerationMixin` inheritance in v4.50
 class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin, PeftAdapterMixin):
     r"""
     Base class for all models.
@@ -1638,11 +1639,30 @@ def can_generate(cls) -> bool:
         Returns:
             `bool`: Whether this model can generate sequences with `.generate()`.
         """
-        # Detects whether `prepare_inputs_for_generation` has been overwritten, which is a requirement for generation.
-        # Alternativelly, the model can also have a custom `generate` function.
-        if "GenerationMixin" in str(cls.prepare_inputs_for_generation) and "GenerationMixin" in str(cls.generate):
-            return False
-        return True
+        # Directly inherits `GenerationMixin` -> can generate
+        if "GenerationMixin" in str(cls.__bases__):
+            return True
+        # Model class overwrites `generate` (e.g. time series models) -> can generate
+        if str(cls.__name__) in str(cls.generate):
+            return True
+        # BC: Detects whether `prepare_inputs_for_generation` has been overwritten in the model. Prior to v4.45, this
+        # was how we detected whether a model could generate.
+        if "GenerationMixin" not in str(cls.prepare_inputs_for_generation):
+            logger.warning_once(
+                f"{cls.__name__} has generative capabilities, as `prepare_inputs_for_generation` is explicitly "
+                "overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, "
+                "`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability "
+                "to call `generate` and other related functions."
+                "\n  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the "
+                "model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes"
+                "\n  - If you are the owner of the model architecture code, please modify your model class such that "
+                "it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception)."
+                "\n  - If you are not the owner of the model architecture class, please contact the model code owner "
+                "to update it."
+            )
+            return True
+        # Otherwise, can't generate
+        return False
 
     @classmethod
     def _check_and_enable_flash_attn_2(
diff --git a/src/transformers/models/albert/modeling_albert.py b/src/transformers/models/albert/modeling_albert.py
index dca1fe7f600295..bfd8e38687accc 100755
--- a/src/transformers/models/albert/modeling_albert.py
+++ b/src/transformers/models/albert/modeling_albert.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -983,7 +984,7 @@ def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
     "Albert Model with a `language modeling` head on top.",
     ALBERT_START_DOCSTRING,
 )
-class AlbertForMaskedLM(AlbertPreTrainedModel):
+class AlbertForMaskedLM(AlbertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/auto/auto_factory.py b/src/transformers/models/auto/auto_factory.py
index 220ae97f5073c6..7809b2a6cc2cfc 100644
--- a/src/transformers/models/auto/auto_factory.py
+++ b/src/transformers/models/auto/auto_factory.py
@@ -29,12 +29,17 @@
     extract_commit_hash,
     find_adapter_config_file,
     is_peft_available,
+    is_torch_available,
     logging,
     requires_backends,
 )
 from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
 
 
+if is_torch_available():
+    from ...generation import GenerationMixin
+
+
 logger = logging.get_logger(__name__)
 
 
@@ -428,6 +433,7 @@ def from_config(cls, config, **kwargs):
             model_class = get_class_from_dynamic_module(class_ref, repo_id, **kwargs)
             cls.register(config.__class__, model_class, exist_ok=True)
             _ = kwargs.pop("code_revision", None)
+            model_class = add_generation_mixin_to_remote_model(model_class)
             return model_class._from_config(config, **kwargs)
         elif type(config) in cls._model_mapping.keys():
             model_class = _get_model_class(config, cls._model_mapping)
@@ -549,6 +555,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
             )
             _ = hub_kwargs.pop("code_revision", None)
             cls.register(config.__class__, model_class, exist_ok=True)
+            model_class = add_generation_mixin_to_remote_model(model_class)
             return model_class.from_pretrained(
                 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
             )
@@ -698,6 +705,34 @@ def getattribute_from_module(module, attr):
         raise ValueError(f"Could not find {attr} in {transformers_module}!")
 
 
+def add_generation_mixin_to_remote_model(model_class):
+    """
+    Adds `GenerationMixin` to the inheritance of `model_class`, if `model_class` is a PyTorch model.
+
+    This function is used for backwards compatibility purposes: in v4.45, we've started a deprecation cycle to make
+    `PreTrainedModel` stop inheriting from `GenerationMixin`. Without this function, older models dynamically loaded
+    from the Hub may not have the `generate` method after we remove the inheritance.
+    """
+    # 1. If it is not a PT model (i.e. doesn't inherit Module), do nothing
+    if "torch.nn.modules.module.Module" not in str(model_class.__mro__):
+        return model_class
+
+    # 2. If it already **directly** inherits from GenerationMixin, do nothing
+    if "GenerationMixin" in str(model_class.__bases__):
+        return model_class
+
+    # 3. Prior to v4.45, we could detect whether a model was `generate`-compatible if it had its own `generate` and/or
+    # `prepare_inputs_for_generation` method.
+    has_custom_generate = "GenerationMixin" not in str(getattr(model_class, "generate"))
+    has_custom_prepare_inputs = "GenerationMixin" not in str(getattr(model_class, "prepare_inputs_for_generation"))
+    if has_custom_generate or has_custom_prepare_inputs:
+        model_class_with_generation_mixin = type(
+            model_class.__name__, (model_class, GenerationMixin), {**model_class.__dict__}
+        )
+        return model_class_with_generation_mixin
+    return model_class
+
+
 class _LazyAutoMapping(OrderedDict):
     """
     " A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed.
diff --git a/src/transformers/models/bark/modeling_bark.py b/src/transformers/models/bark/modeling_bark.py
index 5aad7b23a8a672..3102ada542d57d 100644
--- a/src/transformers/models/bark/modeling_bark.py
+++ b/src/transformers/models/bark/modeling_bark.py
@@ -22,6 +22,7 @@
 from torch import nn
 from torch.nn import functional as F
 
+from ...generation import GenerationMixin
 from ...generation.logits_process import (
     AlternatingCodebooksLogitsProcessor,
     BarkEosPrioritizerLogitsProcessor,
@@ -546,7 +547,7 @@ def device(self) -> torch.device:
 
 
 # GPT2-like autoregressive model
-class BarkCausalModel(BarkPreTrainedModel):
+class BarkCausalModel(BarkPreTrainedModel, GenerationMixin):
     config_class = BarkSubModelConfig
 
     def __init__(self, config):
diff --git a/src/transformers/models/bart/modeling_bart.py b/src/transformers/models/bart/modeling_bart.py
index fa928d05caa89b..2e4e6dcaeb2d11 100755
--- a/src/transformers/models/bart/modeling_bart.py
+++ b/src/transformers/models/bart/modeling_bart.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask,
     _prepare_4d_attention_mask_for_sdpa,
@@ -1557,7 +1558,7 @@ def forward(
 @add_start_docstrings(
     "The BART Model with a language modeling head. Can be used for summarization.", BART_START_DOCSTRING
 )
-class BartForConditionalGeneration(BartPreTrainedModel):
+class BartForConditionalGeneration(BartPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
@@ -2010,7 +2011,7 @@ def forward(self, *args, **kwargs):
     """,
     BART_START_DOCSTRING,
 )
-class BartForCausalLM(BartPreTrainedModel):
+class BartForCausalLM(BartPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/bert/modeling_bert.py b/src/transformers/models/bert/modeling_bert.py
index 93d6d469b51226..b62746da5c6f15 100755
--- a/src/transformers/models/bert/modeling_bert.py
+++ b/src/transformers/models/bert/modeling_bert.py
@@ -28,6 +28,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask_for_sdpa,
     _prepare_4d_causal_attention_mask_for_sdpa,
@@ -1280,7 +1281,7 @@ def forward(
 @add_start_docstrings(
     """Bert Model with a `language modeling` head on top for CLM fine-tuning.""", BERT_START_DOCSTRING
 )
-class BertLMHeadModel(BertPreTrainedModel):
+class BertLMHeadModel(BertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/bert_generation/modeling_bert_generation.py b/src/transformers/models/bert_generation/modeling_bert_generation.py
index a5fb3d0531153e..8496d1f6072f02 100755
--- a/src/transformers/models/bert_generation/modeling_bert_generation.py
+++ b/src/transformers/models/bert_generation/modeling_bert_generation.py
@@ -23,6 +23,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
@@ -863,7 +864,7 @@ def _tie_weights(self):
     """BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.""",
     BERT_GENERATION_START_DOCSTRING,
 )
-class BertGenerationDecoder(BertGenerationPreTrainedModel):
+class BertGenerationDecoder(BertGenerationPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/big_bird/modeling_big_bird.py b/src/transformers/models/big_bird/modeling_big_bird.py
index a6b1660d5ae1e2..41045cb5f0001f 100755
--- a/src/transformers/models/big_bird/modeling_big_bird.py
+++ b/src/transformers/models/big_bird/modeling_big_bird.py
@@ -26,6 +26,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -2495,7 +2496,7 @@ def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_
 @add_start_docstrings(
     """BigBird Model with a `language modeling` head on top for CLM fine-tuning.""", BIG_BIRD_START_DOCSTRING
 )
-class BigBirdForCausalLM(BigBirdPreTrainedModel):
+class BigBirdForCausalLM(BigBirdPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py b/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
index 9f8e3cd19cd835..e26dce1edfc20f 100755
--- a/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
+++ b/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -2436,7 +2437,7 @@ def forward(
     BIGBIRD_PEGASUS_START_DOCSTRING,
 )
 # Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
-class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel):
+class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
@@ -2882,7 +2883,7 @@ def forward(self, *args, **kwargs):
         return self.decoder(*args, **kwargs)
 
 
-class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
+class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/biogpt/modeling_biogpt.py b/src/transformers/models/biogpt/modeling_biogpt.py
index 16f7aab5c3dfa5..7ad1dcbd661c32 100755
--- a/src/transformers/models/biogpt/modeling_biogpt.py
+++ b/src/transformers/models/biogpt/modeling_biogpt.py
@@ -23,6 +23,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
@@ -719,7 +720,7 @@ def forward(
 @add_start_docstrings(
     """BioGPT Model with a `language modeling` head on top for CLM fine-tuning.""", BIOGPT_START_DOCSTRING
 )
-class BioGptForCausalLM(BioGptPreTrainedModel):
+class BioGptForCausalLM(BioGptPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["output_projection.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/blenderbot/modeling_blenderbot.py b/src/transformers/models/blenderbot/modeling_blenderbot.py
index 12d259fde71ec5..4ea5926d854c98 100755
--- a/src/transformers/models/blenderbot/modeling_blenderbot.py
+++ b/src/transformers/models/blenderbot/modeling_blenderbot.py
@@ -26,6 +26,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1196,7 +1197,7 @@ def forward(
 @add_start_docstrings(
     "The Blenderbot Model with a language modeling head. Can be used for summarization.", BLENDERBOT_START_DOCSTRING
 )
-class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
+class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
     _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
@@ -1397,7 +1398,7 @@ def forward(self, *args, **kwargs):
 
 
 # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Blenderbot, facebook/bart-base->facebook/blenderbot-400M-distill
-class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
+class BlenderbotForCausalLM(BlenderbotPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
index aa0e38bd8e9148..3e378f483a317a 100755
--- a/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
+++ b/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1163,7 +1164,7 @@ def forward(
     "The BlenderbotSmall Model with a language modeling head. Can be used for summarization.",
     BLENDERBOT_SMALL_START_DOCSTRING,
 )
-class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
+class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
     _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
@@ -1349,7 +1350,7 @@ def forward(self, *args, **kwargs):
 
 
 # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->BlenderbotSmall, facebook/bart-base->facebook/blenderbot_small-90M
-class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
+class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/blip/modeling_blip.py b/src/transformers/models/blip/modeling_blip.py
index 2392961037f211..aef9b8cebec91f 100644
--- a/src/transformers/models/blip/modeling_blip.py
+++ b/src/transformers/models/blip/modeling_blip.py
@@ -24,6 +24,7 @@
 from torch.nn.functional import normalize
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
@@ -1035,7 +1036,7 @@ def forward(
     """,
     BLIP_START_DOCSTRING,
 )
-class BlipForConditionalGeneration(BlipPreTrainedModel):
+class BlipForConditionalGeneration(BlipPreTrainedModel, GenerationMixin):
     config_class = BlipConfig
     _tied_weights_keys = ["text_decoder.cls.predictions.decoder.bias"]
     main_input_name = "pixel_values"
diff --git a/src/transformers/models/blip/modeling_blip_text.py b/src/transformers/models/blip/modeling_blip_text.py
index a800ba89825dcb..78384e6ce2f74b 100644
--- a/src/transformers/models/blip/modeling_blip_text.py
+++ b/src/transformers/models/blip/modeling_blip_text.py
@@ -23,6 +23,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -808,7 +809,7 @@ def forward(
 
 
 # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L811
-class BlipTextLMHeadModel(BlipTextPreTrainedModel):
+class BlipTextLMHeadModel(BlipTextPreTrainedModel, GenerationMixin):
     def __init__(self, config):
         super().__init__(config)
 
diff --git a/src/transformers/models/blip_2/modeling_blip_2.py b/src/transformers/models/blip_2/modeling_blip_2.py
index 8c3b5254ea8b90..0b33572a689c2a 100644
--- a/src/transformers/models/blip_2/modeling_blip_2.py
+++ b/src/transformers/models/blip_2/modeling_blip_2.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -2006,7 +2007,7 @@ def forward(
     """,
     BLIP_2_START_DOCSTRING,
 )
-class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
+class Blip2ForConditionalGeneration(Blip2PreTrainedModel, GenerationMixin):
     config_class = Blip2Config
     main_input_name = "pixel_values"
 
diff --git a/src/transformers/models/bloom/modeling_bloom.py b/src/transformers/models/bloom/modeling_bloom.py
index b5b221b6b37f83..0992a5519f953d 100644
--- a/src/transformers/models/bloom/modeling_bloom.py
+++ b/src/transformers/models/bloom/modeling_bloom.py
@@ -26,6 +26,7 @@
 
 from ...cache_utils import Cache, DynamicCache, StaticCache
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
@@ -860,7 +861,7 @@ def _update_causal_mask(
     """,
     BLOOM_START_DOCSTRING,
 )
-class BloomForCausalLM(BloomPreTrainedModel):
+class BloomForCausalLM(BloomPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: BloomConfig):
diff --git a/src/transformers/models/camembert/modeling_camembert.py b/src/transformers/models/camembert/modeling_camembert.py
index 0d12c800c156f7..95540f96d3b6f6 100644
--- a/src/transformers/models/camembert/modeling_camembert.py
+++ b/src/transformers/models/camembert/modeling_camembert.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask_for_sdpa,
     _prepare_4d_causal_attention_mask_for_sdpa,
@@ -1544,7 +1545,7 @@ def forward(
     """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING
 )
 # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->Camembert, ROBERTA->CAMEMBERT, FacebookAI/roberta-base->almanach/camembert-base
-class CamembertForCausalLM(CamembertPreTrainedModel):
+class CamembertForCausalLM(CamembertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/chameleon/modeling_chameleon.py b/src/transformers/models/chameleon/modeling_chameleon.py
index 23334311ca9511..c631181f00c59e 100644
--- a/src/transformers/models/chameleon/modeling_chameleon.py
+++ b/src/transformers/models/chameleon/modeling_chameleon.py
@@ -26,6 +26,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
@@ -1496,7 +1497,7 @@ def _update_causal_mask(
     "Chameleon Model with a head on top used for outputting logits for next token prediction.",
     CHAMELEON_START_DOCSTRING,
 )
-class ChameleonForConditionalGeneration(ChameleonPreTrainedModel):
+class ChameleonForConditionalGeneration(ChameleonPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/clvp/modeling_clvp.py b/src/transformers/models/clvp/modeling_clvp.py
index 479b0fac2b0490..f438226064ec2d 100644
--- a/src/transformers/models/clvp/modeling_clvp.py
+++ b/src/transformers/models/clvp/modeling_clvp.py
@@ -26,7 +26,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...generation import GenerationConfig
+from ...generation import GenerationConfig, GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1278,7 +1278,7 @@ def forward(
     "The CLVP decoder model with a language modelling head on top.",
     CLVP_START_DOCSTRING,
 )
-class ClvpForCausalLM(ClvpPreTrainedModel):
+class ClvpForCausalLM(ClvpPreTrainedModel, GenerationMixin):
     def __init__(self, config):
         super().__init__(config)
 
@@ -1509,7 +1509,7 @@ def _reorder_cache(
     "together to filter out the best speech_ids.",
     CLVP_START_DOCSTRING,
 )
-class ClvpModelForConditionalGeneration(ClvpPreTrainedModel):
+class ClvpModelForConditionalGeneration(ClvpPreTrainedModel, GenerationMixin):
     config_class = ClvpConfig
 
     def __init__(self, config: ClvpConfig):
diff --git a/src/transformers/models/codegen/modeling_codegen.py b/src/transformers/models/codegen/modeling_codegen.py
index be57838975c0b0..7d6f64d6461a2e 100644
--- a/src/transformers/models/codegen/modeling_codegen.py
+++ b/src/transformers/models/codegen/modeling_codegen.py
@@ -23,6 +23,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
@@ -702,7 +703,7 @@ def _update_causal_mask(
     """,
     CODEGEN_START_DOCSTRING,
 )
-class CodeGenForCausalLM(CodeGenPreTrainedModel):
+class CodeGenForCausalLM(CodeGenPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/cohere/modeling_cohere.py b/src/transformers/models/cohere/modeling_cohere.py
index cb1b3f885798c8..12586af23f0d7b 100644
--- a/src/transformers/models/cohere/modeling_cohere.py
+++ b/src/transformers/models/cohere/modeling_cohere.py
@@ -32,6 +32,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1068,7 +1069,7 @@ def _update_causal_mask(
 
 
 # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with Llama->Cohere
-class CohereForCausalLM(CoherePreTrainedModel):
+class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     # Ignore copy
diff --git a/src/transformers/models/cpmant/modeling_cpmant.py b/src/transformers/models/cpmant/modeling_cpmant.py
index c8a313505251fb..964d0bbfd1456b 100755
--- a/src/transformers/models/cpmant/modeling_cpmant.py
+++ b/src/transformers/models/cpmant/modeling_cpmant.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
@@ -736,7 +737,7 @@ def forward(
     """,
     CPMANT_START_DOCSTRING,
 )
-class CpmAntForCausalLM(CpmAntPreTrainedModel):
+class CpmAntForCausalLM(CpmAntPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: CpmAntConfig):
diff --git a/src/transformers/models/ctrl/modeling_ctrl.py b/src/transformers/models/ctrl/modeling_ctrl.py
index bbf3b10a62ec1c..6d921621d47dcb 100644
--- a/src/transformers/models/ctrl/modeling_ctrl.py
+++ b/src/transformers/models/ctrl/modeling_ctrl.py
@@ -22,6 +22,7 @@
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer
@@ -503,7 +504,7 @@ def forward(
     """,
     CTRL_START_DOCSTRING,
 )
-class CTRLLMHeadModel(CTRLPreTrainedModel):
+class CTRLLMHeadModel(CTRLPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/data2vec/modeling_data2vec_text.py b/src/transformers/models/data2vec/modeling_data2vec_text.py
index a41fdfb56ed10a..fcddeab7a595ea 100644
--- a/src/transformers/models/data2vec/modeling_data2vec_text.py
+++ b/src/transformers/models/data2vec/modeling_data2vec_text.py
@@ -23,6 +23,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -866,7 +867,7 @@ def forward(
 @add_start_docstrings(
     """Data2VecText Model with a `language modeling` head on top for CLM fine-tuning.""", DATA2VECTEXT_START_DOCSTRING
 )
-class Data2VecTextForCausalLM(Data2VecTextPreTrainedModel):
+class Data2VecTextForCausalLM(Data2VecTextPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/dbrx/modeling_dbrx.py b/src/transformers/models/dbrx/modeling_dbrx.py
index 7263713c084007..46de60e24f1a04 100644
--- a/src/transformers/models/dbrx/modeling_dbrx.py
+++ b/src/transformers/models/dbrx/modeling_dbrx.py
@@ -23,6 +23,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_utils import PreTrainedModel
@@ -1227,7 +1228,7 @@ def _update_causal_mask(
 
 
 @add_start_docstrings("The DBRX Model transformer for causal language modeling.", DBRX_START_DOCSTRING)
-class DbrxForCausalLM(DbrxPreTrainedModel):
+class DbrxForCausalLM(DbrxPreTrainedModel, GenerationMixin):
     def __init__(self, config: DbrxConfig):
         super().__init__(config)
         self.transformer = DbrxModel(config)
diff --git a/src/transformers/models/electra/modeling_electra.py b/src/transformers/models/electra/modeling_electra.py
index dd017170bef9a3..a200d716d451e2 100644
--- a/src/transformers/models/electra/modeling_electra.py
+++ b/src/transformers/models/electra/modeling_electra.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, get_activation
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithCrossAttentions,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1524,7 +1525,7 @@ def forward(
 @add_start_docstrings(
     """ELECTRA Model with a `language modeling` head on top for CLM fine-tuning.""", ELECTRA_START_DOCSTRING
 )
-class ElectraForCausalLM(ElectraPreTrainedModel):
+class ElectraForCausalLM(ElectraPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["generator_lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/ernie/modeling_ernie.py b/src/transformers/models/ernie/modeling_ernie.py
index 6a0a26a5cbe56b..6d81c97da02302 100644
--- a/src/transformers/models/ernie/modeling_ernie.py
+++ b/src/transformers/models/ernie/modeling_ernie.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -1081,7 +1082,7 @@ def forward(
 @add_start_docstrings(
     """Ernie Model with a `language modeling` head on top for CLM fine-tuning.""", ERNIE_START_DOCSTRING
 )
-class ErnieForCausalLM(ErniePreTrainedModel):
+class ErnieForCausalLM(ErniePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
 
     # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->ErnieForCausalLM,Bert->Ernie,bert->ernie
diff --git a/src/transformers/models/falcon/modeling_falcon.py b/src/transformers/models/falcon/modeling_falcon.py
index 9a37fe22e1779e..270845c20aae2e 100644
--- a/src/transformers/models/falcon/modeling_falcon.py
+++ b/src/transformers/models/falcon/modeling_falcon.py
@@ -25,6 +25,7 @@
 
 from ...activations import get_activation
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     AttentionMaskConverter,
 )
@@ -1239,7 +1240,7 @@ def _update_causal_mask(
     "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
     FALCON_START_DOCSTRING,
 )
-class FalconForCausalLM(FalconPreTrainedModel):
+class FalconForCausalLM(FalconPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: FalconConfig):
diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index f682f75f222ef6..011197d9854273 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -25,6 +25,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import MambaCache
+from ...generation import GenerationMixin
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
@@ -717,7 +718,7 @@ def forward(
     FALCONMAMBA_START_DOCSTRING,
 )
 # Copied from transformers.models.mamba.modeling_mamba.MambaForCausalLM with MAMBA->FALCONMAMBA,Mamba->FalconMamba,mamba->falcon_mamba,FalconMambaCache->MambaCache
-class FalconMambaForCausalLM(FalconMambaPreTrainedModel):
+class FalconMambaForCausalLM(FalconMambaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/flaubert/modeling_flaubert.py b/src/transformers/models/flaubert/modeling_flaubert.py
index 50c6f7ede2229f..ef1501e780350d 100644
--- a/src/transformers/models/flaubert/modeling_flaubert.py
+++ b/src/transformers/models/flaubert/modeling_flaubert.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import gelu
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     MaskedLMOutput,
@@ -644,7 +645,7 @@ def forward(
     FLAUBERT_START_DOCSTRING,
 )
 # Copied transformers.models.xlm.modeling_xlm.XLMWithLMHeadModel with XLM_INPUTS->FLAUBERT_INPUTS,XLM->Flaubert
-class FlaubertWithLMHeadModel(FlaubertPreTrainedModel):
+class FlaubertWithLMHeadModel(FlaubertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["pred_layer.proj.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/fsmt/modeling_fsmt.py b/src/transformers/models/fsmt/modeling_fsmt.py
index 179408aba38ebe..4d50f9bb5925b4 100644
--- a/src/transformers/models/fsmt/modeling_fsmt.py
+++ b/src/transformers/models/fsmt/modeling_fsmt.py
@@ -35,6 +35,7 @@
 from torch.nn import CrossEntropyLoss, LayerNorm
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1173,7 +1174,7 @@ def set_output_embeddings(self, value):
 @add_start_docstrings(
     "The FSMT Model with a language modeling head. Can be used for summarization.", FSMT_START_DOCSTRING
 )
-class FSMTForConditionalGeneration(PretrainedFSMTModel):
+class FSMTForConditionalGeneration(PretrainedFSMTModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
 
diff --git a/src/transformers/models/fuyu/modeling_fuyu.py b/src/transformers/models/fuyu/modeling_fuyu.py
index 089313b03b7b60..0aabbf6b3654b7 100644
--- a/src/transformers/models/fuyu/modeling_fuyu.py
+++ b/src/transformers/models/fuyu/modeling_fuyu.py
@@ -20,6 +20,7 @@
 import torch.utils.checkpoint
 from torch import nn
 
+from ...generation import GenerationMixin
 from ...modeling_outputs import CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...models.auto.modeling_auto import AutoModelForCausalLM
@@ -145,7 +146,7 @@ def _init_weights(self, module):
     "Fuyu Model with a language modeling head on top for causal language model conditioned on image patches and text.",
     FUYU_START_DOCSTRING,
 )
-class FuyuForCausalLM(FuyuPreTrainedModel):
+class FuyuForCausalLM(FuyuPreTrainedModel, GenerationMixin):
     def __init__(self, config: FuyuConfig):
         super().__init__(config)
         self.padding_idx = config.pad_token_id
diff --git a/src/transformers/models/gemma/diff_gemma.py b/src/transformers/models/gemma/diff_gemma.py
index 36f2d1c594abaa..dcc43bc74aece9 100644
--- a/src/transformers/models/gemma/diff_gemma.py
+++ b/src/transformers/models/gemma/diff_gemma.py
@@ -34,6 +34,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import CausalLMOutputWithPast
 from ...pytorch_utils import ALL_LAYERNORM_LAYERS
@@ -527,7 +528,7 @@ def forward(
 
 
 # Example where we ony modify the docstring and call super
-class GemmaForCausalLM(LlamaForCausalLM):
+class GemmaForCausalLM(LlamaForCausalLM, GenerationMixin):
     def forward(
         self,
         input_ids: torch.LongTensor = None,
diff --git a/src/transformers/models/gemma/modeling_gemma.py b/src/transformers/models/gemma/modeling_gemma.py
index dd4c899d13d465..8d9bb88686de24 100644
--- a/src/transformers/models/gemma/modeling_gemma.py
+++ b/src/transformers/models/gemma/modeling_gemma.py
@@ -29,6 +29,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
@@ -988,7 +989,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class GemmaForCausalLM(GemmaPreTrainedModel):
+class GemmaForCausalLM(GemmaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gemma2/diff_gemma2.py b/src/transformers/models/gemma2/diff_gemma2.py
index 30f371a1b61267..a66ce3160b5fd1 100644
--- a/src/transformers/models/gemma2/diff_gemma2.py
+++ b/src/transformers/models/gemma2/diff_gemma2.py
@@ -33,6 +33,7 @@
 )
 
 from ...cache_utils import Cache
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging
 
@@ -473,7 +474,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class Gemma2ForCausalLM(GemmaForCausalLM):
+class Gemma2ForCausalLM(GemmaForCausalLM, GenerationMixin):
     def forward(
         self,
         input_ids: torch.LongTensor = None,
diff --git a/src/transformers/models/gemma2/modeling_gemma2.py b/src/transformers/models/gemma2/modeling_gemma2.py
index be964c9aed018a..6b55500739b40b 100644
--- a/src/transformers/models/gemma2/modeling_gemma2.py
+++ b/src/transformers/models/gemma2/modeling_gemma2.py
@@ -28,6 +28,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, HybridCache
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
     CausalLMOutputWithPast,
@@ -931,7 +932,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class Gemma2ForCausalLM(Gemma2PreTrainedModel):
+class Gemma2ForCausalLM(Gemma2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/git/modeling_git.py b/src/transformers/models/git/modeling_git.py
index 7471eec811a065..59d3a406ec3535 100644
--- a/src/transformers/models/git/modeling_git.py
+++ b/src/transformers/models/git/modeling_git.py
@@ -27,6 +27,7 @@
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
 from ...file_utils import ModelOutput
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1324,7 +1325,7 @@ def forward(
 @add_start_docstrings(
     """GIT Model with a `language modeling` head on top for autoregressive language modeling.""", GIT_START_DOCSTRING
 )
-class GitForCausalLM(GitPreTrainedModel):
+class GitForCausalLM(GitPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["output.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gpt2/modeling_gpt2.py b/src/transformers/models/gpt2/modeling_gpt2.py
index 8dfbfb9064444d..e99f4b126246d8 100644
--- a/src/transformers/models/gpt2/modeling_gpt2.py
+++ b/src/transformers/models/gpt2/modeling_gpt2.py
@@ -28,6 +28,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask_for_sdpa
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1182,7 +1183,7 @@ def forward(
     """,
     GPT2_START_DOCSTRING,
 )
-class GPT2LMHeadModel(GPT2PreTrainedModel):
+class GPT2LMHeadModel(GPT2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
@@ -1384,7 +1385,7 @@ def _reorder_cache(
 """,
     GPT2_START_DOCSTRING,
 )
-class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
+class GPT2DoubleHeadsModel(GPT2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py b/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
index 0f927a72469dc9..ca1c03fcd9f911 100644
--- a/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
+++ b/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
@@ -22,6 +22,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1040,7 +1041,7 @@ def forward(
     """,
     GPT_BIGCODE_START_DOCSTRING,
 )
-class GPTBigCodeForCausalLM(GPTBigCodePreTrainedModel):
+class GPTBigCodeForCausalLM(GPTBigCodePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gpt_neo/modeling_gpt_neo.py b/src/transformers/models/gpt_neo/modeling_gpt_neo.py
index 28309f7738ebe2..2fae1753154ccf 100755
--- a/src/transformers/models/gpt_neo/modeling_gpt_neo.py
+++ b/src/transformers/models/gpt_neo/modeling_gpt_neo.py
@@ -24,6 +24,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -917,7 +918,7 @@ def _update_causal_mask(
     """,
     GPT_NEO_START_DOCSTRING,
 )
-class GPTNeoForCausalLM(GPTNeoPreTrainedModel):
+class GPTNeoForCausalLM(GPTNeoPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gpt_neox/modeling_gpt_neox.py b/src/transformers/models/gpt_neox/modeling_gpt_neox.py
index 274c571fa8939c..c1b2aa899985c8 100755
--- a/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+++ b/src/transformers/models/gpt_neox/modeling_gpt_neox.py
@@ -30,6 +30,7 @@
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1110,7 +1111,7 @@ def _update_causal_mask(
 @add_start_docstrings(
     """GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_NEOX_START_DOCSTRING
 )
-class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel):
+class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["embed_out.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py b/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
index 048e108a8ec2d7..3db2099511bc6b 100755
--- a/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
+++ b/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
@@ -25,6 +25,7 @@
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
 from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
@@ -815,7 +816,7 @@ def _update_causal_mask(
     """GPTNeoXJapanese Model with a `language modeling` head on top for Classifier Model fine-tuning.""",
     GPT_NEOX_JAPANESE_START_DOCSTRING,
 )
-class GPTNeoXJapaneseForCausalLM(GPTNeoXJapanesePreTrainedModel):
+class GPTNeoXJapaneseForCausalLM(GPTNeoXJapanesePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["embed_out.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/gptj/modeling_gptj.py b/src/transformers/models/gptj/modeling_gptj.py
index 84f6d985f76474..9eeb26c5e403e0 100644
--- a/src/transformers/models/gptj/modeling_gptj.py
+++ b/src/transformers/models/gptj/modeling_gptj.py
@@ -25,6 +25,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1011,7 +1012,7 @@ def _update_causal_mask(
     """,
     GPTJ_START_DOCSTRING,
 )
-class GPTJForCausalLM(GPTJPreTrainedModel):
+class GPTJForCausalLM(GPTJPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/granite/modeling_granite.py b/src/transformers/models/granite/modeling_granite.py
index f62de411a4fa5f..9a8d4570e7befe 100644
--- a/src/transformers/models/granite/modeling_granite.py
+++ b/src/transformers/models/granite/modeling_granite.py
@@ -22,6 +22,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
@@ -1004,7 +1005,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class GraniteForCausalLM(GranitePreTrainedModel):
+class GraniteForCausalLM(GranitePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Granite
diff --git a/src/transformers/models/granitemoe/modeling_granitemoe.py b/src/transformers/models/granitemoe/modeling_granitemoe.py
index 3ac462bdad34db..d724485990b938 100644
--- a/src/transformers/models/granitemoe/modeling_granitemoe.py
+++ b/src/transformers/models/granitemoe/modeling_granitemoe.py
@@ -23,6 +23,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
@@ -1234,7 +1235,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class GraniteMoeForCausalLM(GraniteMoePreTrainedModel):
+class GraniteMoeForCausalLM(GraniteMoePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: GraniteMoeConfig):
diff --git a/src/transformers/models/idefics2/modeling_idefics2.py b/src/transformers/models/idefics2/modeling_idefics2.py
index 41be300095e710..9273d91ac401ff 100644
--- a/src/transformers/models/idefics2/modeling_idefics2.py
+++ b/src/transformers/models/idefics2/modeling_idefics2.py
@@ -23,11 +23,12 @@
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
 from ...modeling_outputs import BaseModelOutput, ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -1450,7 +1451,7 @@ def forward(
     """The Idefics2 Model with a language modeling head. It is made up a SigLIP vision encoder, with a language modeling head on top. """,
     IDEFICS2_START_DOCSTRING,
 )
-class Idefics2ForConditionalGeneration(Idefics2PreTrainedModel):
+class Idefics2ForConditionalGeneration(Idefics2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/imagegpt/modeling_imagegpt.py b/src/transformers/models/imagegpt/modeling_imagegpt.py
index 5d59a4ed90e4c9..a027876b43d369 100755
--- a/src/transformers/models/imagegpt/modeling_imagegpt.py
+++ b/src/transformers/models/imagegpt/modeling_imagegpt.py
@@ -26,6 +26,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
@@ -880,7 +881,7 @@ def forward(
     """,
     IMAGEGPT_START_DOCSTRING,
 )
-class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel):
+class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: ImageGPTConfig):
diff --git a/src/transformers/models/instructblip/modeling_instructblip.py b/src/transformers/models/instructblip/modeling_instructblip.py
index ba77afe9f7c211..dff897f59d2d26 100644
--- a/src/transformers/models/instructblip/modeling_instructblip.py
+++ b/src/transformers/models/instructblip/modeling_instructblip.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1283,7 +1284,7 @@ def forward(
     """,
     INSTRUCTBLIP_START_DOCSTRING,
 )
-class InstructBlipForConditionalGeneration(InstructBlipPreTrainedModel):
+class InstructBlipForConditionalGeneration(InstructBlipPreTrainedModel, GenerationMixin):
     config_class = InstructBlipConfig
     main_input_name = "pixel_values"
 
diff --git a/src/transformers/models/instructblipvideo/diff_instructblipvideo.py b/src/transformers/models/instructblipvideo/diff_instructblipvideo.py
index 506da83c532240..be569abc9137c2 100644
--- a/src/transformers/models/instructblipvideo/diff_instructblipvideo.py
+++ b/src/transformers/models/instructblipvideo/diff_instructblipvideo.py
@@ -45,6 +45,7 @@
     InstructBlipVisionModel,
 )
 
+from ...generation import GenerationMixin
 from ...utils import logging
 
 
@@ -128,7 +129,7 @@ class InstructBlipVideoQFormerModel(InstructBlipQFormerModel):
     pass
 
 
-class InstructBlipVideoForConditionalGeneration(InstructBlipForConditionalGeneration):
+class InstructBlipVideoForConditionalGeneration(InstructBlipForConditionalGeneration, GenerationMixin):
     def forward(
         self,
         pixel_values: torch.FloatTensor,
diff --git a/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py b/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py
index 8cb813e0ac571c..bcc299b1ba7831 100644
--- a/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py
+++ b/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py
@@ -30,6 +30,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1292,7 +1293,7 @@ def forward(
     """,
     INSTRUCTBLIPVIDEO_START_DOCSTRING,
 )
-class InstructBlipVideoForConditionalGeneration(InstructBlipVideoPreTrainedModel):
+class InstructBlipVideoForConditionalGeneration(InstructBlipVideoPreTrainedModel, GenerationMixin):
     config_class = InstructBlipVideoConfig
     main_input_name = "pixel_values"
 
diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index 60e1670a3c2784..4b8630efbfa946 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -30,6 +30,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache  # we need __iter__ and __len__ of pkv
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     AttentionMaskConverter,
 )
@@ -1424,7 +1425,7 @@ def _update_mamba_mask(self, attention_mask, cache_position):
 
 
 # Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->Jamba
-class JambaForCausalLM(JambaPreTrainedModel):
+class JambaForCausalLM(JambaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: JambaConfig):
diff --git a/src/transformers/models/jetmoe/modeling_jetmoe.py b/src/transformers/models/jetmoe/modeling_jetmoe.py
index 7c4394d0e1a168..e9c06960499136 100644
--- a/src/transformers/models/jetmoe/modeling_jetmoe.py
+++ b/src/transformers/models/jetmoe/modeling_jetmoe.py
@@ -25,6 +25,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     MoeCausalLMOutputWithPast,
@@ -1202,7 +1203,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class JetMoeForCausalLM(JetMoePreTrainedModel):
+class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/kosmos2/modeling_kosmos2.py b/src/transformers/models/kosmos2/modeling_kosmos2.py
index 69641790b2db8f..90e21ed2f5582b 100644
--- a/src/transformers/models/kosmos2/modeling_kosmos2.py
+++ b/src/transformers/models/kosmos2/modeling_kosmos2.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1521,7 +1522,7 @@ def forward(
     """,
     KOSMOS2_START_DOCSTRING,
 )
-class Kosmos2TextForCausalLM(Kosmos2PreTrainedModel):
+class Kosmos2TextForCausalLM(Kosmos2PreTrainedModel, GenerationMixin):
     config_class = Kosmos2TextConfig
     _tied_weights_keys = ["lm_head.weight"]
 
@@ -1864,7 +1865,7 @@ def forward(
     """,
     KOSMOS2_START_DOCSTRING,
 )
-class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel):
+class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel, GenerationMixin):
     config_class = Kosmos2Config
     main_input_name = "pixel_values"
     _tied_weights_keys = ["text_model.lm_head.weight"]
diff --git a/src/transformers/models/led/modeling_led.py b/src/transformers/models/led/modeling_led.py
index 41b6c0a2bea27d..f96bfd82b52638 100755
--- a/src/transformers/models/led/modeling_led.py
+++ b/src/transformers/models/led/modeling_led.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
@@ -2298,7 +2299,7 @@ def forward(
 @add_start_docstrings(
     "The LED Model with a language modeling head. Can be used for summarization.", LED_START_DOCSTRING
 )
-class LEDForConditionalGeneration(LEDPreTrainedModel):
+class LEDForConditionalGeneration(LEDPreTrainedModel, GenerationMixin):
     base_model_prefix = "led"
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
     _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index 0bc44f314b5e86..73b6bcd8b4a4d7 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -28,6 +28,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
@@ -1101,7 +1102,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class LlamaForCausalLM(LlamaPreTrainedModel):
+class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/llava/modeling_llava.py b/src/transformers/models/llava/modeling_llava.py
index eb1c55341b0784..092008873d1e27 100644
--- a/src/transformers/models/llava/modeling_llava.py
+++ b/src/transformers/models/llava/modeling_llava.py
@@ -21,9 +21,10 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -237,7 +238,7 @@ def _supports_sdpa(self):
     """The LLAVA model which consists of a vision backbone and a language model.""",
     LLAVA_START_DOCSTRING,
 )
-class LlavaForConditionalGeneration(LlavaPreTrainedModel):
+class LlavaForConditionalGeneration(LlavaPreTrainedModel, GenerationMixin):
     def __init__(self, config: LlavaConfig):
         super().__init__(config)
         self.vision_tower = AutoModel.from_config(config.vision_config)
diff --git a/src/transformers/models/llava_next/modeling_llava_next.py b/src/transformers/models/llava_next/modeling_llava_next.py
index bf76921090b244..a96b0d89420437 100644
--- a/src/transformers/models/llava_next/modeling_llava_next.py
+++ b/src/transformers/models/llava_next/modeling_llava_next.py
@@ -23,10 +23,11 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...image_processing_utils import select_best_resolution
 from ...modeling_outputs import ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -349,7 +350,7 @@ def _supports_sdpa(self):
     """The LLAVA-NeXT model which consists of a vision backbone and a language model.""",
     LLAVA_NEXT_START_DOCSTRING,
 )
-class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel):
+class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixin):
     def __init__(self, config: LlavaNextConfig):
         super().__init__(config)
         self.vision_tower = AutoModel.from_config(config.vision_config)
diff --git a/src/transformers/models/llava_next_video/diff_llava_next_video.py b/src/transformers/models/llava_next_video/diff_llava_next_video.py
index e765dfb95cc335..c5ca2bf00324d4 100644
--- a/src/transformers/models/llava_next_video/diff_llava_next_video.py
+++ b/src/transformers/models/llava_next_video/diff_llava_next_video.py
@@ -29,6 +29,7 @@
     image_size_to_num_patches,
 )
 
+from ...generation import GenerationMixin
 from ...utils import (
     logging,
     replace_return_docstrings,
@@ -218,7 +219,7 @@ class LlavaNextVideoMultiModalProjector(LlavaNextMultiModalProjector):
     pass
 
 
-class LlavaNextVideoForConditionalGeneration(LlavaNextForConditionalGeneration):
+class LlavaNextVideoForConditionalGeneration(LlavaNextForConditionalGeneration, GenerationMixin):
     def __init__(self, config: LlavaNextVideoConfig, **super_kwargs):
         super().__init__(config, **super_kwargs)
         self.vision_resampler = LlavaNextVideoPooler(config)
diff --git a/src/transformers/models/llava_next_video/modeling_llava_next_video.py b/src/transformers/models/llava_next_video/modeling_llava_next_video.py
index 589bf346ceeb9e..7ad9e0769eb35e 100644
--- a/src/transformers/models/llava_next_video/modeling_llava_next_video.py
+++ b/src/transformers/models/llava_next_video/modeling_llava_next_video.py
@@ -29,10 +29,11 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...image_processing_utils import select_best_resolution
 from ...modeling_outputs import ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -387,7 +388,7 @@ def _supports_sdpa(self):
     """The LLAVA-NeXT model which consists of a vision backbone and a language model.""",
     LLAVA_NEXT_VIDEO_START_DOCSTRING,
 )
-class LlavaNextVideoForConditionalGeneration(LlavaNextVideoPreTrainedModel):
+class LlavaNextVideoForConditionalGeneration(LlavaNextVideoPreTrainedModel, GenerationMixin):
     def __init__(
         self,
         config: LlavaNextVideoConfig,
diff --git a/src/transformers/models/llava_onevision/modeling_llava_onevision.py b/src/transformers/models/llava_onevision/modeling_llava_onevision.py
index 593500c2e404e1..948efbc922b70d 100644
--- a/src/transformers/models/llava_onevision/modeling_llava_onevision.py
+++ b/src/transformers/models/llava_onevision/modeling_llava_onevision.py
@@ -23,10 +23,11 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...image_processing_utils import select_best_resolution
 from ...modeling_outputs import ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     logging,
@@ -358,7 +359,7 @@ def _init_weights(self, module):
     """The LLaVA-Onevision model which consists of a vision backbone and a language model.""",
     LLAVA_ONEVISION_START_DOCSTRING,
 )
-class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel):
+class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin):
     def __init__(self, config: LlavaOnevisionConfig):
         super().__init__(config)
         self.vision_tower = AutoModel.from_config(
diff --git a/src/transformers/models/longt5/modeling_longt5.py b/src/transformers/models/longt5/modeling_longt5.py
index b2a6ed11ca5728..8f9385c0fe76ed 100644
--- a/src/transformers/models/longt5/modeling_longt5.py
+++ b/src/transformers/models/longt5/modeling_longt5.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1900,7 +1901,7 @@ def forward(
 
 
 @add_start_docstrings("""LONGT5 Model with a `language modeling` head on top.""", LONGT5_START_DOCSTRING)
-class LongT5ForConditionalGeneration(LongT5PreTrainedModel):
+class LongT5ForConditionalGeneration(LongT5PreTrainedModel, GenerationMixin):
     _keys_to_ignore_on_load_unexpected = [
         r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
     ]
diff --git a/src/transformers/models/m2m_100/modeling_m2m_100.py b/src/transformers/models/m2m_100/modeling_m2m_100.py
index 23a855fff25672..86a4378da29cdb 100755
--- a/src/transformers/models/m2m_100/modeling_m2m_100.py
+++ b/src/transformers/models/m2m_100/modeling_m2m_100.py
@@ -22,6 +22,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
@@ -1342,7 +1343,7 @@ def forward(
 @add_start_docstrings(
     "The M2M100 Model with a language modeling head. Can be used for summarization.", M2M_100_START_DOCSTRING
 )
-class M2M100ForConditionalGeneration(M2M100PreTrainedModel):
+class M2M100ForConditionalGeneration(M2M100PreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
 
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index 14a3dea1d1ccf8..6bed1caab23ab7 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -25,6 +25,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import MambaCache
+from ...generation import GenerationMixin
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
@@ -657,7 +658,7 @@ def forward(
     """,
     MAMBA_START_DOCSTRING,
 )
-class MambaForCausalLM(MambaPreTrainedModel):
+class MambaForCausalLM(MambaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index 19d53437130e24..01074af38a510b 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
@@ -932,7 +933,7 @@ def forward(
     """,
     MAMBA2_START_DOCSTRING,
 )
-class Mamba2ForCausalLM(Mamba2PreTrainedModel):
+class Mamba2ForCausalLM(Mamba2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = []
 
     def __init__(self, config):
diff --git a/src/transformers/models/marian/modeling_marian.py b/src/transformers/models/marian/modeling_marian.py
index 2045f673540f52..cb26bb11e094cd 100755
--- a/src/transformers/models/marian/modeling_marian.py
+++ b/src/transformers/models/marian/modeling_marian.py
@@ -25,6 +25,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1224,7 +1225,7 @@ def forward(
 @add_start_docstrings(
     "The Marian Model with a language modeling head. Can be used for summarization.", MARIAN_START_DOCSTRING
 )
-class MarianMTModel(MarianPreTrainedModel):
+class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = [
         "final_logits_bias",
@@ -1504,7 +1505,7 @@ def forward(self, *args, **kwargs):
 
 
 # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Marian, facebook/bart-base->Helsinki-NLP/opus-mt-fr-en
-class MarianForCausalLM(MarianPreTrainedModel):
+class MarianForCausalLM(MarianPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/mbart/modeling_mbart.py b/src/transformers/models/mbart/modeling_mbart.py
index 9455f21b2073ff..3f2d6cb8e2ba8d 100755
--- a/src/transformers/models/mbart/modeling_mbart.py
+++ b/src/transformers/models/mbart/modeling_mbart.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask,
     _prepare_4d_attention_mask_for_sdpa,
@@ -1526,7 +1527,7 @@ def forward(
     "The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models.",
     MBART_START_DOCSTRING,
 )
-class MBartForConditionalGeneration(MBartPreTrainedModel):
+class MBartForConditionalGeneration(MBartPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
     _tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
@@ -1967,7 +1968,7 @@ def forward(self, *args, **kwargs):
 
 
 # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->MBart, facebook/bart-base->facebook/mbart-large-cc25
-class MBartForCausalLM(MBartPreTrainedModel):
+class MBartForCausalLM(MBartPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/megatron_bert/modeling_megatron_bert.py b/src/transformers/models/megatron_bert/modeling_megatron_bert.py
index 16641655e20395..20506f91bcbcb2 100755
--- a/src/transformers/models/megatron_bert/modeling_megatron_bert.py
+++ b/src/transformers/models/megatron_bert/modeling_megatron_bert.py
@@ -27,6 +27,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -1110,7 +1111,7 @@ def forward(
     """MegatronBert Model with a `language modeling` head on top for CLM fine-tuning.""",
     MEGATRON_BERT_START_DOCSTRING,
 )
-class MegatronBertForCausalLM(MegatronBertPreTrainedModel):
+class MegatronBertForCausalLM(MegatronBertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
index 80992734046ad6..ffa1a18307e982 100644
--- a/src/transformers/models/mistral/modeling_mistral.py
+++ b/src/transformers/models/mistral/modeling_mistral.py
@@ -29,6 +29,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -950,7 +951,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class MistralForCausalLM(MistralPreTrainedModel):
+class MistralForCausalLM(MistralPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/mixtral/modeling_mixtral.py b/src/transformers/models/mixtral/modeling_mixtral.py
index fcc0d66e19c4a4..a1786fbb17e3c5 100644
--- a/src/transformers/models/mixtral/modeling_mixtral.py
+++ b/src/transformers/models/mixtral/modeling_mixtral.py
@@ -30,6 +30,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     MoeCausalLMOutputWithPast,
@@ -1186,7 +1187,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class MixtralForCausalLM(MixtralPreTrainedModel):
+class MixtralForCausalLM(MixtralPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/mpt/modeling_mpt.py b/src/transformers/models/mpt/modeling_mpt.py
index 85579636dcc4cd..9c826c370b752a 100644
--- a/src/transformers/models/mpt/modeling_mpt.py
+++ b/src/transformers/models/mpt/modeling_mpt.py
@@ -24,6 +24,7 @@
 from torch.nn import functional as F
 
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
@@ -500,7 +501,7 @@ def forward(
     """,
     MPT_START_DOCSTRING,
 )
-class MptForCausalLM(MptPreTrainedModel):
+class MptForCausalLM(MptPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config: MptConfig):
diff --git a/src/transformers/models/mt5/modeling_mt5.py b/src/transformers/models/mt5/modeling_mt5.py
index 54943cf982dd24..6a7406f11b5b56 100644
--- a/src/transformers/models/mt5/modeling_mt5.py
+++ b/src/transformers/models/mt5/modeling_mt5.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1550,7 +1551,7 @@ def forward(
 
 
 @add_start_docstrings("""MT5 Model with a `language modeling` head on top.""", MT5_START_DOCSTRING)
-class MT5ForConditionalGeneration(MT5PreTrainedModel):
+class MT5ForConditionalGeneration(MT5PreTrainedModel, GenerationMixin):
     r"""
     Examples:
 
diff --git a/src/transformers/models/musicgen/modeling_musicgen.py b/src/transformers/models/musicgen/modeling_musicgen.py
index f720faac038e51..3109c4fc243118 100644
--- a/src/transformers/models/musicgen/modeling_musicgen.py
+++ b/src/transformers/models/musicgen/modeling_musicgen.py
@@ -26,9 +26,14 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...generation.configuration_utils import GenerationConfig, GenerationMode
-from ...generation.logits_process import ClassifierFreeGuidanceLogitsProcessor, LogitsProcessorList
-from ...generation.stopping_criteria import StoppingCriteriaList
+from ...generation import (
+    ClassifierFreeGuidanceLogitsProcessor,
+    GenerationConfig,
+    GenerationMixin,
+    GenerationMode,
+    LogitsProcessorList,
+    StoppingCriteriaList,
+)
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask,
     _prepare_4d_attention_mask_for_sdpa,
@@ -1206,7 +1211,7 @@ def forward(
     "The MusicGen decoder model with a language modelling head on top.",
     MUSICGEN_START_DOCSTRING,
 )
-class MusicgenForCausalLM(MusicgenPreTrainedModel):
+class MusicgenForCausalLM(MusicgenPreTrainedModel, GenerationMixin):
     def __init__(self, config: MusicgenDecoderConfig):
         super().__init__(config)
 
@@ -1658,7 +1663,7 @@ def generate(
     "for music generation tasks with one or both of text and audio prompts.",
     MUSICGEN_START_DOCSTRING,
 )
-class MusicgenForConditionalGeneration(PreTrainedModel):
+class MusicgenForConditionalGeneration(PreTrainedModel, GenerationMixin):
     config_class = MusicgenConfig
     base_model_prefix = "encoder_decoder"
     main_input_name = "input_ids"
diff --git a/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py b/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
index a8a8fe96098952..c8345870b2537e 100644
--- a/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
+++ b/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
@@ -26,9 +26,14 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...generation.configuration_utils import GenerationConfig, GenerationMode
-from ...generation.logits_process import ClassifierFreeGuidanceLogitsProcessor, LogitsProcessorList
-from ...generation.stopping_criteria import StoppingCriteriaList
+from ...generation import (
+    ClassifierFreeGuidanceLogitsProcessor,
+    GenerationConfig,
+    GenerationMixin,
+    GenerationMode,
+    LogitsProcessorList,
+    StoppingCriteriaList,
+)
 from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1117,7 +1122,7 @@ def forward(
     MUSICGEN_MELODY_START_DOCSTRING,
 )
 # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForCausalLM with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody,MusicGen->Musicgen Melody
-class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
+class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel, GenerationMixin):
     def __init__(self, config: MusicgenMelodyDecoderConfig):
         super().__init__(config)
 
@@ -1585,7 +1590,7 @@ def generate(
         decoder (`Optional[MusicgenMelodyForCausalLM]`, *optional*): MusicGen Melody decoder used to generate audio codes.
     """,
 )
-class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
+class MusicgenMelodyForConditionalGeneration(PreTrainedModel, GenerationMixin):
     config_class = MusicgenMelodyConfig
     main_input_name = "input_ids"
     supports_gradient_checkpointing = True
diff --git a/src/transformers/models/mvp/modeling_mvp.py b/src/transformers/models/mvp/modeling_mvp.py
index 319f1760cef9df..c47c4b26b539f7 100644
--- a/src/transformers/models/mvp/modeling_mvp.py
+++ b/src/transformers/models/mvp/modeling_mvp.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1351,7 +1352,7 @@ def forward(
 @add_start_docstrings(
     "The MVP Model with a language modeling head. Can be used for various text generation tasks.", MVP_START_DOCSTRING
 )
-class MvpForConditionalGeneration(MvpPreTrainedModel):
+class MvpForConditionalGeneration(MvpPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
 
     def __init__(self, config: MvpConfig):
@@ -1791,7 +1792,7 @@ def forward(self, *args, **kwargs):
         return self.decoder(*args, **kwargs)
 
 
-class MvpForCausalLM(MvpPreTrainedModel):
+class MvpForCausalLM(MvpPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/nemotron/modeling_nemotron.py b/src/transformers/models/nemotron/modeling_nemotron.py
index 4d079b4dde104d..aa699853d55762 100644
--- a/src/transformers/models/nemotron/modeling_nemotron.py
+++ b/src/transformers/models/nemotron/modeling_nemotron.py
@@ -26,6 +26,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_flash_attention_utils import _flash_attention_forward
 from ...modeling_outputs import (
@@ -980,7 +981,7 @@ def _update_causal_mask(
 
 
 # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
-class NemotronForCausalLM(NemotronPreTrainedModel):
+class NemotronForCausalLM(NemotronPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/nllb_moe/modeling_nllb_moe.py b/src/transformers/models/nllb_moe/modeling_nllb_moe.py
index 2bec0fb84dce56..c33844da0f55b8 100644
--- a/src/transformers/models/nllb_moe/modeling_nllb_moe.py
+++ b/src/transformers/models/nllb_moe/modeling_nllb_moe.py
@@ -22,6 +22,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
@@ -1604,7 +1605,7 @@ def forward(
 @add_start_docstrings(
     "The NllbMoe Model with a language modeling head. Can be used for summarization.", NLLB_MOE_START_DOCSTRING
 )
-class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel):
+class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
 
diff --git a/src/transformers/models/olmo/modeling_olmo.py b/src/transformers/models/olmo/modeling_olmo.py
index 03fa524532a02e..a44b7d2a0a4c4d 100644
--- a/src/transformers/models/olmo/modeling_olmo.py
+++ b/src/transformers/models/olmo/modeling_olmo.py
@@ -30,6 +30,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1022,7 +1023,7 @@ def _update_causal_mask(
 
 
 # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->OLMO,Llama->Olmo
-class OlmoForCausalLM(OlmoPreTrainedModel):
+class OlmoForCausalLM(OlmoPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/olmoe/modeling_olmoe.py b/src/transformers/models/olmoe/modeling_olmoe.py
index 2cbde7dc863169..d30cace3a7055d 100644
--- a/src/transformers/models/olmoe/modeling_olmoe.py
+++ b/src/transformers/models/olmoe/modeling_olmoe.py
@@ -22,6 +22,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     MoeCausalLMOutputWithPast,
@@ -1173,7 +1174,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class OlmoeForCausalLM(OlmoePreTrainedModel):
+class OlmoeForCausalLM(OlmoePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/openai/modeling_openai.py b/src/transformers/models/openai/modeling_openai.py
index 2b24850f3f0c89..0aa02a6f5d8424 100644
--- a/src/transformers/models/openai/modeling_openai.py
+++ b/src/transformers/models/openai/modeling_openai.py
@@ -26,6 +26,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import gelu_new, silu
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel, SequenceSummary
 from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
@@ -524,7 +525,7 @@ def forward(
     """,
     OPENAI_GPT_START_DOCSTRING,
 )
-class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
+class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/opt/modeling_opt.py b/src/transformers/models/opt/modeling_opt.py
index 8f058171778efc..f7782b8f6172b9 100644
--- a/src/transformers/models/opt/modeling_opt.py
+++ b/src/transformers/models/opt/modeling_opt.py
@@ -22,6 +22,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -882,7 +883,7 @@ def forward(
         )
 
 
-class OPTForCausalLM(OPTPreTrainedModel):
+class OPTForCausalLM(OPTPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/paligemma/modeling_paligemma.py b/src/transformers/models/paligemma/modeling_paligemma.py
index 48fffb6b428df7..b5fddce1d6a914 100644
--- a/src/transformers/models/paligemma/modeling_paligemma.py
+++ b/src/transformers/models/paligemma/modeling_paligemma.py
@@ -22,6 +22,7 @@
 from torch import nn
 
 from ...cache_utils import Cache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
@@ -302,7 +303,7 @@ def _supports_sdpa(self):
     """The PALIGEMMA model which consists of a vision backbone and a language model.""",
     PALIGEMMA_START_DOCSTRING,
 )
-class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel):
+class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixin):
     def __init__(self, config: PaliGemmaConfig):
         super().__init__(config)
         self.vision_tower = AutoModel.from_config(config=config.vision_config)
diff --git a/src/transformers/models/pegasus/modeling_pegasus.py b/src/transformers/models/pegasus/modeling_pegasus.py
index 42cef3a63558e2..03d1574e9be28e 100755
--- a/src/transformers/models/pegasus/modeling_pegasus.py
+++ b/src/transformers/models/pegasus/modeling_pegasus.py
@@ -25,6 +25,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1244,7 +1245,7 @@ def forward(
 @add_start_docstrings(
     "The PEGASUS Model with a language modeling head. Can be used for summarization.", PEGASUS_START_DOCSTRING
 )
-class PegasusForConditionalGeneration(PegasusPreTrainedModel):
+class PegasusForConditionalGeneration(PegasusPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
@@ -1456,7 +1457,7 @@ def forward(self, *args, **kwargs):
         return self.decoder(*args, **kwargs)
 
 
-class PegasusForCausalLM(PegasusPreTrainedModel):
+class PegasusForCausalLM(PegasusPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/pegasus_x/modeling_pegasus_x.py b/src/transformers/models/pegasus_x/modeling_pegasus_x.py
index 6d9072777bf634..77c0b32e6433c4 100755
--- a/src/transformers/models/pegasus_x/modeling_pegasus_x.py
+++ b/src/transformers/models/pegasus_x/modeling_pegasus_x.py
@@ -25,6 +25,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1464,7 +1465,7 @@ def forward(
 
 
 @add_start_docstrings("The PEGASUS-X for conditional generation (e.g. summarization).", PEGASUS_X_START_DOCSTRING)
-class PegasusXForConditionalGeneration(PegasusXPreTrainedModel):
+class PegasusXForConditionalGeneration(PegasusXPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
 
diff --git a/src/transformers/models/persimmon/modeling_persimmon.py b/src/transformers/models/persimmon/modeling_persimmon.py
index a6fd2284afb691..4f122e14284d83 100644
--- a/src/transformers/models/persimmon/modeling_persimmon.py
+++ b/src/transformers/models/persimmon/modeling_persimmon.py
@@ -29,6 +29,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -847,7 +848,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class PersimmonForCausalLM(PersimmonPreTrainedModel):
+class PersimmonForCausalLM(PersimmonPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->PERSIMMON,Llama->Persimmon
diff --git a/src/transformers/models/phi/modeling_phi.py b/src/transformers/models/phi/modeling_phi.py
index 4d0a076b5f9a33..03ed19bc34ac84 100644
--- a/src/transformers/models/phi/modeling_phi.py
+++ b/src/transformers/models/phi/modeling_phi.py
@@ -26,6 +26,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1139,7 +1140,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class PhiForCausalLM(PhiPreTrainedModel):
+class PhiForCausalLM(PhiPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
diff --git a/src/transformers/models/phi3/modeling_phi3.py b/src/transformers/models/phi3/modeling_phi3.py
index e0ca84be184843..12ee9f017f8149 100644
--- a/src/transformers/models/phi3/modeling_phi3.py
+++ b/src/transformers/models/phi3/modeling_phi3.py
@@ -26,6 +26,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1160,7 +1161,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class Phi3ForCausalLM(Phi3PreTrainedModel):
+class Phi3ForCausalLM(Phi3PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
diff --git a/src/transformers/models/pix2struct/modeling_pix2struct.py b/src/transformers/models/pix2struct/modeling_pix2struct.py
index 94d882c80566ad..f209d7d8828785 100644
--- a/src/transformers/models/pix2struct/modeling_pix2struct.py
+++ b/src/transformers/models/pix2struct/modeling_pix2struct.py
@@ -22,6 +22,7 @@
 from torch import nn
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPooling,
@@ -1553,7 +1554,7 @@ def forward(
     "A conditional generation model with a language modeling head. Can be used for sequence generation tasks.",
     PIX2STRUCT_START_DOCSTRING,
 )
-class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel):
+class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel, GenerationMixin):
     config_class = Pix2StructConfig
     main_input_name = "flattened_patches"
     _tied_weights_keys = ["decoder.lm_head.weight"]
diff --git a/src/transformers/models/plbart/modeling_plbart.py b/src/transformers/models/plbart/modeling_plbart.py
index 93d91e160089e1..d15e079770a3ed 100644
--- a/src/transformers/models/plbart/modeling_plbart.py
+++ b/src/transformers/models/plbart/modeling_plbart.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask,
     _prepare_4d_attention_mask_for_sdpa,
@@ -1254,7 +1255,7 @@ def forward(
     "The PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code.",
     PLBART_START_DOCSTRING,
 )
-class PLBartForConditionalGeneration(PLBartPreTrainedModel):
+class PLBartForConditionalGeneration(PLBartPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _keys_to_ignore_on_load_missing = ["final_logits_bias"]
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
@@ -1568,7 +1569,7 @@ def forward(self, *args, **kwargs):
 
 
 # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->PLBart, facebook/bart-base->uclanlp/plbart-base
-class PLBartForCausalLM(PLBartPreTrainedModel):
+class PLBartForCausalLM(PLBartPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/pop2piano/modeling_pop2piano.py b/src/transformers/models/pop2piano/modeling_pop2piano.py
index c769cff3c454ec..e6488898e8a938 100644
--- a/src/transformers/models/pop2piano/modeling_pop2piano.py
+++ b/src/transformers/models/pop2piano/modeling_pop2piano.py
@@ -25,6 +25,7 @@
 from transformers.generation import GenerationConfig
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1001,7 +1002,7 @@ def forward(self, feature, index_value, embedding_offset):
 
 
 @add_start_docstrings("""Pop2Piano Model with a `language modeling` head on top.""", Pop2Piano_START_DOCSTRING)
-class Pop2PianoForConditionalGeneration(Pop2PianoPreTrainedModel):
+class Pop2PianoForConditionalGeneration(Pop2PianoPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
 
     def __init__(self, config: Pop2PianoConfig):
diff --git a/src/transformers/models/prophetnet/modeling_prophetnet.py b/src/transformers/models/prophetnet/modeling_prophetnet.py
index 96fa2e2c12e52f..7d23088f6e575e 100644
--- a/src/transformers/models/prophetnet/modeling_prophetnet.py
+++ b/src/transformers/models/prophetnet/modeling_prophetnet.py
@@ -26,6 +26,7 @@
 from torch.nn import LayerNorm
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
@@ -1856,7 +1857,7 @@ def forward(
     "The ProphetNet Model with a language modeling head. Can be used for sequence generation tasks.",
     PROPHETNET_START_DOCSTRING,
 )
-class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel):
+class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight", "lm_head.weight"]
 
     def __init__(self, config: ProphetNetConfig):
@@ -2073,7 +2074,7 @@ def get_decoder(self):
     " language modeling.",
     PROPHETNET_START_DOCSTRING,
 )
-class ProphetNetForCausalLM(ProphetNetPreTrainedModel):
+class ProphetNetForCausalLM(ProphetNetPreTrainedModel, GenerationMixin):
     _tied_weights_keys = [
         "prophetnet.word_embeddings.weight",
         "prophetnet.decoder.word_embeddings.weight",
diff --git a/src/transformers/models/qwen2/modeling_qwen2.py b/src/transformers/models/qwen2/modeling_qwen2.py
index 1e79115d34701b..10c0b6f38669da 100644
--- a/src/transformers/models/qwen2/modeling_qwen2.py
+++ b/src/transformers/models/qwen2/modeling_qwen2.py
@@ -29,6 +29,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1078,7 +1079,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class Qwen2ForCausalLM(Qwen2PreTrainedModel):
+class Qwen2ForCausalLM(Qwen2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py b/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
index 14235bf0aaf64a..bf48e1c6a97ef9 100644
--- a/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
+++ b/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
@@ -22,10 +22,11 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
 from ...cache_utils import Cache, EncoderDecoderCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutput, ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -855,7 +856,7 @@ def forward(self, audio_features):
     """The QWEN2AUDIO model which consists of a audio backbone and a language model.""",
     QWEN2AUDIO_START_DOCSTRING,
 )
-class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel):
+class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel, GenerationMixin):
     def __init__(self, config: Qwen2AudioConfig):
         super().__init__(config)
         self.audio_tower = AutoModel.from_config(config.audio_config, attn_implementation=config._attn_implementation)
diff --git a/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py b/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
index c9ee7b5f57a1f2..1b28e9baf25f66 100644
--- a/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
+++ b/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
@@ -30,6 +30,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     MoeCausalLMOutputWithPast,
@@ -1253,7 +1254,7 @@ def _update_causal_mask(
         return causal_mask
 
 
-class Qwen2MoeForCausalLM(Qwen2MoePreTrainedModel):
+class Qwen2MoeForCausalLM(Qwen2MoePreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py b/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
index 02716153fddaee..938ec4d5e42362 100644
--- a/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
+++ b/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
@@ -31,6 +31,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     AttentionMaskConverter,
 )
@@ -1416,7 +1417,7 @@ def _update_causal_mask(
 """
 
 
-class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel):
+class Qwen2VLForConditionalGeneration(Qwen2VLPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py b/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py
index a8f076fad79c76..e0492948998434 100644
--- a/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py
+++ b/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import BaseModelOutputWithNoAttention, CausalLMOutput
 from ...modeling_utils import PreTrainedModel
@@ -777,7 +778,7 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
 
 
 # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->RECURRENTGEMMA,Llama->RecurrentGemma,llama->gemma
-class RecurrentGemmaForCausalLM(RecurrentGemmaPreTrainedModel):
+class RecurrentGemmaForCausalLM(RecurrentGemmaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/reformer/modeling_reformer.py b/src/transformers/models/reformer/modeling_reformer.py
index 2e98a07217e682..37b675539e66cf 100755
--- a/src/transformers/models/reformer/modeling_reformer.py
+++ b/src/transformers/models/reformer/modeling_reformer.py
@@ -29,6 +29,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import CausalLMOutput, MaskedLMOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import apply_chunking_to_forward
@@ -2183,7 +2184,7 @@ def _pad_to_mult_of_chunk_length(
 
 
 @add_start_docstrings("""Reformer Model with a `language modeling` head on top.""", REFORMER_START_DOCSTRING)
-class ReformerModelWithLMHead(ReformerPreTrainedModel):
+class ReformerModelWithLMHead(ReformerPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/rembert/modeling_rembert.py b/src/transformers/models/rembert/modeling_rembert.py
index 31f7e3dce4548c..99016c1be429cd 100755
--- a/src/transformers/models/rembert/modeling_rembert.py
+++ b/src/transformers/models/rembert/modeling_rembert.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -1002,7 +1003,7 @@ def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_
 @add_start_docstrings(
     """RemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", REMBERT_START_DOCSTRING
 )
-class RemBertForCausalLM(RemBertPreTrainedModel):
+class RemBertForCausalLM(RemBertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/roberta/modeling_roberta.py b/src/transformers/models/roberta/modeling_roberta.py
index bbf16ec039b4cd..91500e1926d753 100644
--- a/src/transformers/models/roberta/modeling_roberta.py
+++ b/src/transformers/models/roberta/modeling_roberta.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask_for_sdpa,
     _prepare_4d_causal_attention_mask_for_sdpa,
@@ -1003,7 +1004,7 @@ def forward(
 @add_start_docstrings(
     """RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.""", ROBERTA_START_DOCSTRING
 )
-class RobertaForCausalLM(RobertaPreTrainedModel):
+class RobertaForCausalLM(RobertaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py b/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py
index 95657c260dc7a4..9ed9b11d9431df 100644
--- a/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py
+++ b/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -855,7 +856,7 @@ def forward(
     ROBERTA_PRELAYERNORM_START_DOCSTRING,
 )
 # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with FacebookAI/roberta-base->andreasmadsen/efficient_mlm_m0.40,ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm, RobertaPreLayerNormTokenizer->RobertaTokenizer
-class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel):
+class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/roc_bert/modeling_roc_bert.py b/src/transformers/models/roc_bert/modeling_roc_bert.py
index c4efbf16323eba..2969f7f1a3d0a8 100644
--- a/src/transformers/models/roc_bert/modeling_roc_bert.py
+++ b/src/transformers/models/roc_bert/modeling_roc_bert.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -1403,7 +1404,7 @@ def prepare_inputs_for_generation(
 @add_start_docstrings(
     """RoCBert Model with a `language modeling` head on top for CLM fine-tuning.""", ROC_BERT_START_DOCSTRING
 )
-class RoCBertForCausalLM(RoCBertPreTrainedModel):
+class RoCBertForCausalLM(RoCBertPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
 
     # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->RoCBertForCausalLM,Bert->RoCBert,bert->roc_bert
diff --git a/src/transformers/models/roformer/modeling_roformer.py b/src/transformers/models/roformer/modeling_roformer.py
index 69588ff743a0e6..c98b525abe081b 100644
--- a/src/transformers/models/roformer/modeling_roformer.py
+++ b/src/transformers/models/roformer/modeling_roformer.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
@@ -1033,7 +1034,7 @@ def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_
 @add_start_docstrings(
     """RoFormer Model with a `language modeling` head on top for CLM fine-tuning.""", ROFORMER_START_DOCSTRING
 )
-class RoFormerForCausalLM(RoFormerPreTrainedModel):
+class RoFormerForCausalLM(RoFormerPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/rwkv/modeling_rwkv.py b/src/transformers/models/rwkv/modeling_rwkv.py
index 7dec1f26e1a390..8361afbf727b64 100644
--- a/src/transformers/models/rwkv/modeling_rwkv.py
+++ b/src/transformers/models/rwkv/modeling_rwkv.py
@@ -25,6 +25,7 @@
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
+from ...generation import GenerationMixin
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
@@ -751,7 +752,7 @@ def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id):
     """,
     RWKV_START_DOCSTRING,
 )
-class RwkvForCausalLM(RwkvPreTrainedModel):
+class RwkvForCausalLM(RwkvPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py b/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py
index ba8230ec509df8..8e226d92a10580 100755
--- a/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py
+++ b/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py
@@ -26,6 +26,7 @@
 
 from ...activations import ACT2FN
 from ...deepspeed import is_deepspeed_zero3_enabled
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -2150,7 +2151,7 @@ def forward(
         embed_tokens_decoder (`nn.Embedding`, *optional*): input embedding of the decoder.
     """,
 )
-class SeamlessM4TTextToUnitForConditionalGeneration(SeamlessM4TPreTrainedModel):
+class SeamlessM4TTextToUnitForConditionalGeneration(SeamlessM4TPreTrainedModel, GenerationMixin):
     _keys_to_ignore_on_load_missing = [
         "vocoder",
         "speech_encoder",
@@ -2664,7 +2665,7 @@ def remove_weight_norm(self):
     "The text-to-text SeamlessM4T Model transformer which can be used for T2TT.",
     SEAMLESS_M4T_START_DOCSTRING,
 )
-class SeamlessM4TForTextToText(SeamlessM4TPreTrainedModel):
+class SeamlessM4TForTextToText(SeamlessM4TPreTrainedModel, GenerationMixin):
     _keys_to_ignore_on_load_missing = ["speech_encoder", "t2u_model", "vocoder"]
     main_input_name = "input_ids"
 
diff --git a/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py b/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py
index 2d1fde8eed69a0..aa710ad95266ff 100644
--- a/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py
+++ b/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py
@@ -26,6 +26,7 @@
 
 from ...activations import ACT2FN
 from ...deepspeed import is_deepspeed_zero3_enabled
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -2439,7 +2440,7 @@ def forward(
         embed_tokens_decoder (`nn.Embedding`, *optional*): input embedding of the decoder.
     """,
 )
-class SeamlessM4Tv2TextToUnitForConditionalGeneration(SeamlessM4Tv2PreTrainedModel):
+class SeamlessM4Tv2TextToUnitForConditionalGeneration(SeamlessM4Tv2PreTrainedModel, GenerationMixin):
     _keys_to_ignore_on_load_missing = [
         "vocoder",
         "speech_encoder",
@@ -2922,7 +2923,7 @@ def remove_weight_norm(self):
     SEAMLESS_M4T_V2_START_DOCSTRING,
 )
 # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToText with SeamlessM4T->SeamlessM4Tv2,SeamlessM4Tv2Tokenizer->SeamlessM4TTokenizer, SeamlessM4Tv2Processor->SeamlessM4TProcessor
-class SeamlessM4Tv2ForTextToText(SeamlessM4Tv2PreTrainedModel):
+class SeamlessM4Tv2ForTextToText(SeamlessM4Tv2PreTrainedModel, GenerationMixin):
     _keys_to_ignore_on_load_missing = ["speech_encoder", "t2u_model", "vocoder"]
     main_input_name = "input_ids"
 
diff --git a/src/transformers/models/speech_to_text/modeling_speech_to_text.py b/src/transformers/models/speech_to_text/modeling_speech_to_text.py
index 8353a172b2120f..bdd532fa25e82a 100755
--- a/src/transformers/models/speech_to_text/modeling_speech_to_text.py
+++ b/src/transformers/models/speech_to_text/modeling_speech_to_text.py
@@ -22,6 +22,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1207,7 +1208,7 @@ def forward(
     "The Speech2Text Model with a language modeling head. Can be used for summarization.",
     SPEECH_TO_TEXT_START_DOCSTRING,
 )
-class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel):
+class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["lm_head.weight"]
 
diff --git a/src/transformers/models/stablelm/modeling_stablelm.py b/src/transformers/models/stablelm/modeling_stablelm.py
index 13641ecb37f255..463a30fabe77c0 100755
--- a/src/transformers/models/stablelm/modeling_stablelm.py
+++ b/src/transformers/models/stablelm/modeling_stablelm.py
@@ -29,6 +29,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1123,7 +1124,7 @@ def _update_causal_mask(
 
 
 # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM with PERSIMMON->STABLELM,Persimmon->StableLm
-class StableLmForCausalLM(StableLmPreTrainedModel):
+class StableLmForCausalLM(StableLmPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->STABLELM,Llama->StableLm
diff --git a/src/transformers/models/starcoder2/modeling_starcoder2.py b/src/transformers/models/starcoder2/modeling_starcoder2.py
index 5eaf50f090fa49..079ad1298fb999 100644
--- a/src/transformers/models/starcoder2/modeling_starcoder2.py
+++ b/src/transformers/models/starcoder2/modeling_starcoder2.py
@@ -29,6 +29,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
@@ -1053,7 +1054,7 @@ def _update_causal_mask(
 
 
 # Copied from transformers.models.qwen2.modeling_qwen2.Qwen2ForCausalLM with QWEN2->STARCODER2,Qwen2->Starcoder2
-class Starcoder2ForCausalLM(Starcoder2PreTrainedModel):
+class Starcoder2ForCausalLM(Starcoder2PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/switch_transformers/modeling_switch_transformers.py b/src/transformers/models/switch_transformers/modeling_switch_transformers.py
index c5797d4573b781..96b6c7334b1535 100644
--- a/src/transformers/models/switch_transformers/modeling_switch_transformers.py
+++ b/src/transformers/models/switch_transformers/modeling_switch_transformers.py
@@ -24,6 +24,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     MoEModelOutput,
     MoEModelOutputWithPastAndCrossAttentions,
@@ -1456,7 +1457,7 @@ def forward(
 @add_start_docstrings(
     """SWITCH_TRANSFORMERS Model with a `language modeling` head on top.""", SWITCH_TRANSFORMERS_START_DOCSTRING
 )
-class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel):
+class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
 
     def __init__(self, config: SwitchTransformersConfig):
diff --git a/src/transformers/models/t5/modeling_t5.py b/src/transformers/models/t5/modeling_t5.py
index a90101924c5ba6..43e3f3afa4a837 100644
--- a/src/transformers/models/t5/modeling_t5.py
+++ b/src/transformers/models/t5/modeling_t5.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1542,7 +1543,7 @@ def forward(
 
 
 @add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
-class T5ForConditionalGeneration(T5PreTrainedModel):
+class T5ForConditionalGeneration(T5PreTrainedModel, GenerationMixin):
     _keys_to_ignore_on_load_unexpected = [
         "decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
     ]
diff --git a/src/transformers/models/trocr/modeling_trocr.py b/src/transformers/models/trocr/modeling_trocr.py
index 04eb40ab2a2f47..67b97cf9c85223 100644
--- a/src/transformers/models/trocr/modeling_trocr.py
+++ b/src/transformers/models/trocr/modeling_trocr.py
@@ -23,6 +23,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
 from ...modeling_utils import PreTrainedModel
@@ -736,7 +737,7 @@ def forward(self, *args, **kwargs):
     " [`VisionEncoderDecoder`].",
     TROCR_START_DOCSTRING,
 )
-class TrOCRForCausalLM(TrOCRPreTrainedModel):
+class TrOCRForCausalLM(TrOCRPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["output_projection.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/udop/modeling_udop.py b/src/transformers/models/udop/modeling_udop.py
index 6f7b6cf060495a..c621b742323db2 100644
--- a/src/transformers/models/udop/modeling_udop.py
+++ b/src/transformers/models/udop/modeling_udop.py
@@ -34,6 +34,7 @@
 )
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
@@ -1679,7 +1680,7 @@ def forward(
     This class is based on [`T5ForConditionalGeneration`], extended to deal with images and layout (2D) data.""",
     UDOP_START_DOCSTRING,
 )
-class UdopForConditionalGeneration(UdopPreTrainedModel):
+class UdopForConditionalGeneration(UdopPreTrainedModel, GenerationMixin):
     _tied_weights_keys = [
         "encoder.embed_tokens.weight",
         "decoder.embed_tokens.weight",
diff --git a/src/transformers/models/umt5/modeling_umt5.py b/src/transformers/models/umt5/modeling_umt5.py
index 3271689540b931..a7d1e5bacc65e5 100644
--- a/src/transformers/models/umt5/modeling_umt5.py
+++ b/src/transformers/models/umt5/modeling_umt5.py
@@ -23,6 +23,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,
@@ -1101,7 +1102,7 @@ def forward(
 
 
 @add_start_docstrings("""UMT5 Model with a `language modeling` head on top.""", UMT5_START_DOCSTRING)
-class UMT5ForConditionalGeneration(UMT5PreTrainedModel):
+class UMT5ForConditionalGeneration(UMT5PreTrainedModel, GenerationMixin):
     r"""
     Examples:
 
diff --git a/src/transformers/models/video_llava/modeling_video_llava.py b/src/transformers/models/video_llava/modeling_video_llava.py
index 9ae80be65ae4b6..7c7cfec20959ea 100644
--- a/src/transformers/models/video_llava/modeling_video_llava.py
+++ b/src/transformers/models/video_llava/modeling_video_llava.py
@@ -21,9 +21,10 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutputWithPooling, ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -239,7 +240,7 @@ def _supports_sdpa(self):
     """The VideoLlava model which consists of a vision backbone and a language model.""",
     VIDEO_LLAVA_START_DOCSTRING,
 )
-class VideoLlavaForConditionalGeneration(VideoLlavaPreTrainedModel):
+class VideoLlavaForConditionalGeneration(VideoLlavaPreTrainedModel, GenerationMixin):
     def __init__(self, config: VideoLlavaConfig):
         super().__init__(config)
         self.video_tower = AutoModel.from_config(config.vision_config)
diff --git a/src/transformers/models/vipllava/modeling_vipllava.py b/src/transformers/models/vipllava/modeling_vipllava.py
index 53a3213697193e..95129d46bbd8e5 100644
--- a/src/transformers/models/vipllava/modeling_vipllava.py
+++ b/src/transformers/models/vipllava/modeling_vipllava.py
@@ -21,9 +21,10 @@
 import torch.utils.checkpoint
 from torch import nn
 
-from ... import PreTrainedModel
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_outputs import ModelOutput
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -240,7 +241,7 @@ def _supports_sdpa(self):
     VIPLLAVA_START_DOCSTRING,
 )
 # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration with LLAVA->VIPLLAVA,Llava->VipLlava
-class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel):
+class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel, GenerationMixin):
     def __init__(self, config: VipLlavaConfig):
         super().__init__(config)
         self.vision_tower = AutoModel.from_config(config.vision_config)
diff --git a/src/transformers/models/whisper/generation_whisper.py b/src/transformers/models/whisper/generation_whisper.py
index 8012c3c1bbfcfb..7a4e9487288e93 100644
--- a/src/transformers/models/whisper/generation_whisper.py
+++ b/src/transformers/models/whisper/generation_whisper.py
@@ -25,7 +25,7 @@
 
 from transformers.cache_utils import EncoderDecoderCache
 
-from ...generation.configuration_utils import GenerationConfig
+from ...generation import GenerationConfig, GenerationMixin
 from ...generation.logits_process import (
     LogitsProcessorList,
     SuppressTokensAtBeginLogitsProcessor,
@@ -172,7 +172,7 @@ def _pad_to_max_length(
     return sequences
 
 
-class WhisperGenerationMixin:
+class WhisperGenerationMixin(GenerationMixin):
     def _extract_token_timestamps(self, generate_outputs, alignment_heads, time_precision=0.02, num_frames=None):
         """
         Calculates token-level timestamps using the encoder-decoder cross-attentions and dynamic time-warping (DTW) to
diff --git a/src/transformers/models/whisper/modeling_whisper.py b/src/transformers/models/whisper/modeling_whisper.py
index b82b978e5e6d95..93ec57fcf4b400 100644
--- a/src/transformers/models/whisper/modeling_whisper.py
+++ b/src/transformers/models/whisper/modeling_whisper.py
@@ -25,6 +25,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import AttentionMaskConverter
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -1915,7 +1916,7 @@ def forward(self, *args, **kwargs):
     """,
     WHISPER_START_DOCSTRING,
 )
-class WhisperForCausalLM(WhisperPreTrainedModel):
+class WhisperForCausalLM(WhisperPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["proj_out.weight"]
     main_input_name = "input_ids"
 
diff --git a/src/transformers/models/xglm/modeling_xglm.py b/src/transformers/models/xglm/modeling_xglm.py
index 4f1693583494ec..3090bc2973cd56 100755
--- a/src/transformers/models/xglm/modeling_xglm.py
+++ b/src/transformers/models/xglm/modeling_xglm.py
@@ -23,6 +23,7 @@
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
 from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
 from ...modeling_utils import PreTrainedModel
@@ -696,7 +697,7 @@ def forward(
     """,
     XGLM_START_DOCSTRING,
 )
-class XGLMForCausalLM(XGLMPreTrainedModel):
+class XGLMForCausalLM(XGLMPreTrainedModel, GenerationMixin):
     base_model_prefix = "model"
     _tied_weights_keys = ["lm_head.weight"]
 
diff --git a/src/transformers/models/xlm/modeling_xlm.py b/src/transformers/models/xlm/modeling_xlm.py
index 2803836309872c..3acec2353b69fc 100755
--- a/src/transformers/models/xlm/modeling_xlm.py
+++ b/src/transformers/models/xlm/modeling_xlm.py
@@ -27,6 +27,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import gelu
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutput,
     MaskedLMOutput,
@@ -657,7 +658,7 @@ def forward(self, x, y=None):
     """,
     XLM_START_DOCSTRING,
 )
-class XLMWithLMHeadModel(XLMPreTrainedModel):
+class XLMWithLMHeadModel(XLMPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["pred_layer.proj.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py b/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py
index 2adae33fbd50a8..a153f09468937b 100644
--- a/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py
+++ b/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py
@@ -25,6 +25,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask_for_sdpa,
     _prepare_4d_causal_attention_mask_for_sdpa,
@@ -1006,7 +1007,7 @@ def forward(
     XLM_ROBERTA_START_DOCSTRING,
 )
 # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with Roberta->XLMRoberta, ROBERTA->XLM_ROBERTA
-class XLMRobertaForCausalLM(XLMRobertaPreTrainedModel):
+class XLMRobertaForCausalLM(XLMRobertaPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py b/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py
index f86abf823e9026..0c384ad45c523b 100644
--- a/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py
+++ b/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py
@@ -24,6 +24,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_attn_mask_utils import (
     _prepare_4d_attention_mask_for_sdpa,
     _prepare_4d_causal_attention_mask_for_sdpa,
@@ -986,7 +987,7 @@ def forward(
     """XLM-RoBERTa-XL Model with a `language modeling` head on top for CLM fine-tuning.""",
     XLM_ROBERTA_XL_START_DOCSTRING,
 )
-class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel):
+class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/xlnet/modeling_xlnet.py b/src/transformers/models/xlnet/modeling_xlnet.py
index 5d424ebe12dd6f..7681fbafad6db5 100755
--- a/src/transformers/models/xlnet/modeling_xlnet.py
+++ b/src/transformers/models/xlnet/modeling_xlnet.py
@@ -26,6 +26,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...generation import GenerationMixin
 from ...modeling_utils import PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits, PreTrainedModel, SequenceSummary
 from ...pytorch_utils import apply_chunking_to_forward
 from ...utils import (
@@ -1286,7 +1287,7 @@ def forward(
     """,
     XLNET_START_DOCSTRING,
 )
-class XLNetLMHeadModel(XLNetPreTrainedModel):
+class XLNetLMHeadModel(XLNetPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_loss.weight"]
 
     def __init__(self, config):
diff --git a/src/transformers/models/xmod/modeling_xmod.py b/src/transformers/models/xmod/modeling_xmod.py
index b1ca8116a72a78..71474cc9c45ba9 100644
--- a/src/transformers/models/xmod/modeling_xmod.py
+++ b/src/transformers/models/xmod/modeling_xmod.py
@@ -23,6 +23,7 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
+from ...generation import GenerationMixin
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -956,7 +957,7 @@ def forward(
     "X-MOD Model with a `language modeling` head on top for CLM fine-tuning.",
     XMOD_START_DOCSTRING,
 )
-class XmodForCausalLM(XmodPreTrainedModel):
+class XmodForCausalLM(XmodPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
 
     # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM.__init__ with Roberta->Xmod
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 2f8e60c79151e9..600942a7ac0822 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2099,6 +2099,15 @@ def test_assisted_decoding_with_num_logits_to_keep(self):
             )
             self.assertEqual(with_all_logits.tolist(), without_all_logits.tolist())
 
+    @pytest.mark.generate
+    def test_inherits_generation_mixin(self):
+        """
+        Tests that the model class directly inherits `GenerationMixin`, as opposed to relying on `PreTrainedModel`
+        to inherit it.
+        """
+        for model_class in self.all_generative_model_classes:
+            self.assertTrue("GenerationMixin" in str(model_class.__bases__))
+
     def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
         batch_size, seq_length = input_ids.shape
         config = config.text_config if hasattr(config, "text_config") else config
diff --git a/tests/models/auto/test_modeling_auto.py b/tests/models/auto/test_modeling_auto.py
index 39770b091befe8..95d716898343d0 100644
--- a/tests/models/auto/test_modeling_auto.py
+++ b/tests/models/auto/test_modeling_auto.py
@@ -67,6 +67,7 @@
         BertModel,
         FunnelBaseModel,
         FunnelModel,
+        GenerationMixin,
         GPT2Config,
         GPT2LMHeadModel,
         ResNetBackbone,
@@ -571,3 +572,20 @@ def test_dynamic_saving_from_local_repo(self):
             _ = AutoModelForCausalLM.from_pretrained(tmp_dir_out, trust_remote_code=True)
             self.assertTrue((Path(tmp_dir_out) / "modeling_fake_custom.py").is_file())
             self.assertTrue((Path(tmp_dir_out) / "configuration_fake_custom.py").is_file())
+
+    def test_custom_model_patched_generation_inheritance(self):
+        """
+        Tests that our inheritance patching for generate-compatible models works as expected. Without this feature,
+        old Hub models lose the ability to call `generate`.
+        """
+        model = AutoModelForCausalLM.from_pretrained(
+            "hf-internal-testing/test_dynamic_model_generation", trust_remote_code=True
+        )
+        self.assertTrue(model.__class__.__name__ == "NewModelForCausalLM")
+
+        # It inherits from GenerationMixin. This means it can `generate`. Because `PreTrainedModel` is scheduled to
+        # stop inheriting from `GenerationMixin` in v4.50, this check will fail if patching is not present.
+        self.assertTrue(isinstance(model, GenerationMixin))
+        # More precisely, it directly inherits from GenerationMixin. This check would fail prior to v4.45 (inheritance
+        # patching was added in v4.45)
+        self.assertTrue("GenerationMixin" in str(model.__class__.__bases__))
diff --git a/tests/utils/test_modeling_utils.py b/tests/utils/test_modeling_utils.py
index 2130ed4b7c887f..5155647059f154 100644
--- a/tests/utils/test_modeling_utils.py
+++ b/tests/utils/test_modeling_utils.py
@@ -90,6 +90,7 @@
         BertConfig,
         BertModel,
         CLIPTextModel,
+        GenerationMixin,
         PreTrainedModel,
         T5Config,
         T5ForConditionalGeneration,
@@ -1715,6 +1716,32 @@ def test_isin_mps_friendly(self):
             torch.equal(torch.isin(random_ids, random_test_tensor), isin_mps_friendly(random_ids, random_test_tensor))
         )
 
+    def test_can_generate(self):
+        """Tests the behavior of `PreTrainedModel.can_generate` method."""
+        # 1 - By default, a model CAN'T generate
+        self.assertFalse(BertModel.can_generate())
+
+        # 2 - The most common case for a model to be able to generate is to inherit from `GenerationMixin` directly
+        class DummyBertWithMixin(BertModel, GenerationMixin):
+            pass
+
+        self.assertTrue(DummyBertWithMixin.can_generate())
+
+        # 3 - Alternatively, a model can implement a `generate` method
+        class DummyBertWithGenerate(BertModel):
+            def generate(self):
+                pass
+
+        self.assertTrue(DummyBertWithGenerate.can_generate())
+
+        # 4 - BC: models with a custom `prepare_inputs_for_generation` can generate (it was assumed they inherited
+        # `GenerationMixin`)
+        class DummyBertWithPrepareInputs(BertModel):
+            def prepare_inputs_for_generation(self):
+                pass
+
+        self.assertTrue(DummyBertWithPrepareInputs.can_generate())
+
     def test_save_and_load_config_with_custom_generation(self):
         """
         Regression test for the ability to save and load a config with a custom generation kwarg (i.e. a parameter