diff --git a/src/transformers/generation/candidate_generator.py b/src/transformers/generation/candidate_generator.py
new file mode 100644
index 000000000000..7cceac3364af
--- /dev/null
+++ b/src/transformers/generation/candidate_generator.py
@@ -0,0 +1,330 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import warnings
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+import torch
+
+
+if TYPE_CHECKING:
+    from ..modeling_utils import PreTrainedModel
+    from .logits_process import LogitsProcessorList
+
+
+class CandidateGenerator:
+    """Abstract base class for all candidate generators that can be applied during assisted generation."""
+
+    def get_candidates(self, input_ids: torch.LongTensor) -> torch.LongTensor:
+        """
+        Fetches the candidates to be tried for the current input.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
+
+        Return:
+            `torch.LongTensor` of shape `(num_candidates, candidate_length)`: The candidate sequences to be assessed by
+            the model.
+        """
+        raise NotImplementedError(
+            f"{self.__class__} is an abstract class. Only classes inheriting this class can call `get_candidates`."
+        )
+
+    def update_candidate_strategy(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, num_matches: int):
+        """
+        Updates the candidate generation strategy based on the outcomes.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
+            scores (`torch.FloatTensor` of shape `(batch_size, candidate_length, config.vocab_size)`):
+                Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
+                beam search or log softmax for each vocabulary token when using beam search
+            num_matches (`int`):
+                The number of matches between the candidate sequences and the model predictions.
+        """
+        raise NotImplementedError(
+            f"{self.__class__} is an abstract class. Only classes inheriting this class can call "
+            "`update_candidate_strategy`."
+        )
+
+
+class AssistedCandidateGenerator(CandidateGenerator):
+    """
+    `CandidateGenerator` class to be used for assisted generation. This class generates candidates through the use of
+    a smaller model. Read the following blog post for more information: https://huggingface.co/blog/assisted-generation
+
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
+        assistant_model (`PreTrainedModel`):
+            The model to be used for generating candidates. This model should be smaller than the main model.
+        logits_processor (`LogitsProcessorList`):
+            An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+            used to modify the prediction scores of the language modeling head applied at each generation step.
+        model_kwargs (`Dict`):
+            The keyword arguments that will be passed to the main model, and are used as base inputs for the assistant
+            model as well.
+        inputs_tensor (`torch.Tensor`, *optional*):
+            The model input tensor. In encoder-decoder models, this is the encoder input.
+        eos_token_id (`Union[int, List[int]]`, *optional*):
+            The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+    """
+
+    def __init__(
+        self,
+        input_ids: torch.LongTensor,
+        assistant_model: "PreTrainedModel",
+        logits_processor: "LogitsProcessorList",
+        model_kwargs: Dict,
+        inputs_tensor: Optional[torch.Tensor] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+    ):
+        self.assistant_model = assistant_model
+
+        # Prepare the number of candidate tokens
+        if hasattr(assistant_model, "num_assistant_tokens"):
+            warnings.warn(
+                "Setting `num_assistant_tokens` via `assistant_model.num_assistant_tokens` is deprecated and will be "
+                "removed in v4.37. Make sure to set `num_assistant_tokens` via the generation_config instead.",
+                FutureWarning,
+            )
+            self.num_assistant_tokens = assistant_model.num_assistant_tokens
+        else:
+            self.num_assistant_tokens = assistant_model.generation_config.num_assistant_tokens
+
+        # Prepare the kwargs for the assistant model
+        assistant_kwargs = {}
+        for key, value in model_kwargs.items():  # deepcopy crashes if we attempt to copy encoder outputs with grads
+            if key not in ("encoder_outputs", "assistant_encoder_outputs"):
+                assistant_kwargs[key] = value.detach() if isinstance(value, torch.Tensor) else copy.deepcopy(value)
+
+        if "assistant_encoder_outputs" in model_kwargs:
+            assistant_kwargs["encoder_outputs"] = model_kwargs["assistant_encoder_outputs"]
+        elif assistant_model.config.is_encoder_decoder:
+            inputs_tensor, model_input_name, assistant_kwargs = assistant_model._prepare_model_inputs(
+                inputs_tensor, assistant_model.generation_config.bos_token_id, assistant_kwargs
+            )
+            assistant_kwargs = assistant_model._prepare_encoder_decoder_kwargs_for_generation(
+                inputs_tensor, assistant_kwargs, model_input_name
+            )
+        elif "encoder_outputs" in model_kwargs:
+            assistant_kwargs["encoder_outputs"] = model_kwargs["encoder_outputs"]
+        self.assistant_kwargs = assistant_kwargs
+
+        # Prepare assistant model's keys of inputs
+        if assistant_model.config.is_encoder_decoder:
+            # both are encoder-decoder
+            self.input_ids_key = "decoder_input_ids"
+            self.attention_key = "decoder_attention_mask"
+        elif "encoder_outputs" in assistant_kwargs:
+            # special case for encoder-decoder with decoder-only assistant (like DistilWhisper)
+            self.input_ids_key = "input_ids"
+            self.attention_key = "attention_mask"
+            self.assistant_kwargs["attention_mask"] = self.assistant_kwargs.get(
+                "decoder_attention_mask",
+                torch.ones((input_ids.shape[0], 1), device=input_ids.device, dtype=torch.long),
+            )
+        else:
+            # both are decoder-only
+            self.input_ids_key = "input_ids"
+            self.attention_key = "attention_mask"
+
+        # Prepare other attributes
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        self.eos_token_id_tensor = (
+            torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
+        )
+        self.logits_processor = logits_processor
+
+    def get_candidates(self, input_ids: torch.LongTensor) -> torch.LongTensor:
+        """
+        Fetches the candidates to be tried for the current input.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
+
+        Return:
+            `torch.LongTensor` of shape `(num_candidates, candidate_length)`: The candidate sequences to be tried.
+        """
+        # 1. If it is not the first round of candidate generation, prepare the inputs based on the input_ids length
+        # (which implicitly contains the number of accepted candidates from the previous round)
+        has_past_key_values = self.assistant_kwargs.get("past_key_values", None) is not None
+        if has_past_key_values:
+            new_cur_len = input_ids.shape[-1]
+
+            new_cache_size = new_cur_len - 1
+            self.assistant_kwargs["past_key_values"] = _crop_past_key_values(
+                self.assistant_model, self.assistant_kwargs["past_key_values"], new_cache_size - 1
+            )  # the assistant does not have the token after the last match, hence the -1
+
+            self.assistant_kwargs = _prepare_attention_mask(
+                self.assistant_kwargs, new_cur_len, self.assistant_model.config.is_encoder_decoder
+            )
+            self.assistant_kwargs = _prepare_token_type_ids(self.assistant_kwargs, new_cur_len)
+
+        # 2. Forecast next N tokens using the assistant model. This `for` block can be replaced with a `.generate()`
+        # call if we decide to add `past_key_values` as a possible output of generate, as we need access to the
+        # assistant cache to secure strong speedups.
+        candidate_input_ids = input_ids
+        for _ in range(int(self.num_assistant_tokens)):
+            # 2.1 prepare assistant model inputs
+            assistant_inputs = self.assistant_model.prepare_inputs_for_generation(
+                candidate_input_ids,
+                **self.assistant_kwargs,
+            )
+
+            # 2.2. check if the input ids length is correct
+            has_past_key_values = assistant_inputs.get("past_key_values", None) is not None
+            if has_past_key_values and assistant_inputs[self.input_ids_key].shape[-1] not in (1, 2):
+                raise ValueError("The length of the input ids in assistant inputs should be 1 or 2")
+
+            # 2.3. use the assistant model to obtain the next candidate logits
+            assistant_model_outputs = self.assistant_model(**assistant_inputs)
+
+            # 2.4. greedily select the next candidate token
+            if len(self.logits_processor) > 0:
+                assistant_model_outputs.logits[:, -1, :] = self.logits_processor(
+                    candidate_input_ids, assistant_model_outputs.logits[:, -1, :]
+                )
+            new_token = assistant_model_outputs.logits[:, -1, :].argmax(dim=-1)
+            candidate_input_ids = torch.cat((candidate_input_ids, new_token[:, None]), dim=-1)
+
+            # 2.5. update assistant model inputs
+            if self.assistant_kwargs.get(self.attention_key, None) is not None:
+                mask = self.assistant_kwargs[self.attention_key]
+                self.assistant_kwargs[self.attention_key] = torch.cat(
+                    [mask, mask.new_ones((mask.shape[0], 1))], dim=-1
+                )
+            self.assistant_kwargs["past_key_values"] = assistant_model_outputs.past_key_values
+
+            # 2.6. stop assistant generation on EOS
+            if self.eos_token_id_tensor is not None:
+                last_assistant_token_is_eos = new_token.tile(self.eos_token_id_tensor.shape[0], 1)
+                last_assistant_token_is_eos = (
+                    ~last_assistant_token_is_eos.ne(self.eos_token_id_tensor.unsqueeze(1)).prod(dim=0).bool()
+                )
+                if last_assistant_token_is_eos:
+                    break
+
+        return candidate_input_ids
+
+    def update_candidate_strategy(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, num_matches: int):
+        """
+        Updates the candidate generation strategy based on the outcomes.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
+            scores (`torch.FloatTensor` of shape `(batch_size, candidate_length, config.vocab_size)`):
+                Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
+                beam search or log softmax for each vocabulary token when using beam search
+            num_matches (`int`):
+                The number of matches between the candidate sequences and the model predictions.
+        """
+        # Adjust the max number of assistant tokens to use in the next iteration. This is a simple heuristic,
+        # probably can be improved -- we want to balance the benefits of getting assistant tokens correct with the
+        # cost of forecasting incorrect assistant tokens.
+        if self.assistant_model.generation_config.num_assistant_tokens_schedule == "heuristic":
+            if num_matches == int(self.num_assistant_tokens):
+                self.num_assistant_tokens += 2.0
+            else:
+                self.num_assistant_tokens = max(1.0, self.num_assistant_tokens - 1.0)
+
+
+def _crop_past_key_values(model, past_key_values, maximum_length):
+    """Crops the past key values up to a certain maximum length."""
+    new_past = []
+    if model.config.is_encoder_decoder:
+        for idx in range(len(past_key_values)):
+            new_past.append(
+                (
+                    past_key_values[idx][0][:, :, :maximum_length, :],
+                    past_key_values[idx][1][:, :, :maximum_length, :],
+                    past_key_values[idx][2],
+                    past_key_values[idx][3],
+                )
+            )
+        past_key_values = tuple(new_past)
+    # bloom is special
+    elif "bloom" in model.__class__.__name__.lower() or (
+        model.config.architectures is not None and "bloom" in model.config.architectures[0].lower()
+    ):
+        for idx in range(len(past_key_values)):
+            new_past.append(
+                (
+                    past_key_values[idx][0][:, :, :maximum_length],
+                    past_key_values[idx][1][:, :maximum_length, :],
+                )
+            )
+        past_key_values = tuple(new_past)
+    # gptbigcode is too
+    elif "gptbigcode" in model.__class__.__name__.lower() or (
+        model.config.architectures is not None and "gptbigcode" in model.config.architectures[0].lower()
+    ):
+        if model.config.multi_query:
+            for idx in range(len(past_key_values)):
+                past_key_values[idx] = past_key_values[idx][:, :maximum_length, :]
+        else:
+            for idx in range(len(past_key_values)):
+                past_key_values[idx] = past_key_values[idx][:, :, :maximum_length, :]
+    else:
+        for idx in range(len(past_key_values)):
+            new_past.append(
+                (
+                    past_key_values[idx][0][:, :, :maximum_length, :],
+                    past_key_values[idx][1][:, :, :maximum_length, :],
+                )
+            )
+        past_key_values = tuple(new_past)
+    return past_key_values
+
+
+def _prepare_attention_mask(model_kwargs: Dict[str, Any], new_length: int, is_encoder_decoder: bool) -> Dict[str, Any]:
+    """Expands or crops the model's mask for decoding purposes, to the defined length"""
+
+    mask_key = "decoder_attention_mask" if is_encoder_decoder else "attention_mask"
+    if mask_key not in model_kwargs:
+        return model_kwargs
+
+    mask = model_kwargs[mask_key]
+    mask_length_diff = new_length - mask.shape[1]
+
+    if mask_length_diff < 0:
+        model_kwargs[mask_key] = mask[:, :mask_length_diff]
+    elif mask_length_diff > 0:
+        model_kwargs[mask_key] = torch.cat([mask, mask.new_ones((mask.shape[0], mask_length_diff))], dim=-1)
+    return model_kwargs
+
+
+def _prepare_token_type_ids(model_kwargs: Dict[str, Any], new_length: int) -> Dict[str, Any]:
+    """Expands or crops the model's token_type_ids for decoding purposes, to the defined length"""
+    if "token_type_ids" not in model_kwargs or model_kwargs["token_type_ids"] is None:
+        return model_kwargs
+
+    token_type_ids = model_kwargs["token_type_ids"]
+    final_token_type = token_type_ids[:, -1].unsqueeze(-1)
+    type_length_diff = new_length - token_type_ids.shape[1]
+
+    if type_length_diff < 0:
+        token_type_ids = token_type_ids[:, :type_length_diff]
+    elif type_length_diff > 0:
+        token_type_copies = final_token_type.repeat(1, type_length_diff)
+        model_kwargs["token_type_ids"] = torch.cat([model_kwargs["token_type_ids"], token_type_copies], dim=-1)
+    return model_kwargs
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 1d413b3ab443..d7510951b116 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -37,6 +37,13 @@
 from ..utils import ExplicitEnum, ModelOutput, is_accelerate_available, logging
 from .beam_constraints import DisjunctiveConstraint, PhrasalConstraint
 from .beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
+from .candidate_generator import (
+    AssistedCandidateGenerator,
+    CandidateGenerator,
+    _crop_past_key_values,
+    _prepare_attention_mask,
+    _prepare_token_type_ids,
+)
 from .configuration_utils import GenerationConfig
 from .logits_process import (
     EncoderNoRepeatNGramLogitsProcessor,
@@ -889,6 +896,28 @@ def _reorder_cache(self, past_key_values, beam_idx):
             f" enable beam search for {self.__class__}"
         )
 
+    def _get_candidate_generator(
+        self,
+        generation_config: GenerationConfig,
+        input_ids: torch.LongTensor,
+        inputs_tensor: torch.Tensor,
+        assistant_model: "PreTrainedModel",
+        logits_processor: LogitsProcessorList,
+        model_kwargs: Dict,
+    ) -> CandidateGenerator:
+        """
+        Returns the candidate generator to be used in `assisted_generation`
+        """
+        candidate_generator = AssistedCandidateGenerator(
+            input_ids=input_ids,
+            assistant_model=assistant_model,
+            logits_processor=logits_processor,
+            model_kwargs=model_kwargs,
+            inputs_tensor=inputs_tensor,
+            eos_token_id=generation_config.eos_token_id,
+        )
+        return candidate_generator
+
     def _get_logits_warper(
         self,
         generation_config: GenerationConfig,
@@ -1671,36 +1700,20 @@ def generate(
             if not model_kwargs["use_cache"]:
                 raise ValueError("assisted generate requires `use_cache=True`")
 
-            assistant_accepts_encoder_outputs = "encoder_outputs" in set(
-                inspect.signature(assistant_model.forward).parameters.keys()
+            # 11. Get the candidate generator, given the parameterization
+            candidate_generator = self._get_candidate_generator(
+                generation_config=generation_config,
+                input_ids=input_ids,
+                inputs_tensor=inputs_tensor,
+                assistant_model=assistant_model,
+                logits_processor=logits_processor,
+                model_kwargs=model_kwargs,
             )
 
-            # 11. If the assistant model is an encoder-decoder, prepare its encoder outputs
-            if assistant_model.config.is_encoder_decoder and "assistant_encoder_outputs" not in model_kwargs:
-                assistant_model_kwargs = copy.deepcopy(model_kwargs)
-                inputs_tensor, model_input_name, assistant_model_kwargs = assistant_model._prepare_model_inputs(
-                    inputs_tensor, assistant_model.generation_config.bos_token_id, assistant_model_kwargs
-                )
-                assistant_model_kwargs = assistant_model._prepare_encoder_decoder_kwargs_for_generation(
-                    inputs_tensor, assistant_model_kwargs, model_input_name
-                )
-                model_kwargs["assistant_encoder_outputs"] = assistant_model_kwargs["encoder_outputs"]
-
-            if (
-                not assistant_model.config.is_encoder_decoder
-                and assistant_accepts_encoder_outputs
-                and "encoder_outputs" in model_kwargs
-            ):
-                # some assistants might be assymetric (many more enc layers than dec layers)
-                # encoder-decoder models that share the exact same encoder as the teacher
-                # in this case the assistant only needs to load the light-weight decoder,
-                # but still requires `encoder_outputs` to be passed
-                model_kwargs["assistant_encoder_outputs"] = model_kwargs["encoder_outputs"]
-
             # 12. run assisted generate
             return self.assisted_decoding(
                 input_ids,
-                assistant_model=assistant_model,
+                candidate_generator=candidate_generator,
                 do_sample=generation_config.do_sample,
                 logits_processor=logits_processor,
                 logits_warper=self._get_logits_warper(generation_config) if generation_config.do_sample else None,
@@ -4378,7 +4391,8 @@ def constrained_beam_search(
     def assisted_decoding(
         self,
         input_ids: torch.LongTensor,
-        assistant_model: "PreTrainedModel",
+        assistant_model: Optional["PreTrainedModel"] = None,
+        candidate_generator: Optional["CandidateGenerator"] = None,
         do_sample: bool = False,
         logits_processor: Optional[LogitsProcessorList] = None,
         logits_warper: Optional[LogitsProcessorList] = None,
@@ -4395,12 +4409,13 @@ def assisted_decoding(
     ):
         r"""
         Generates sequences of token ids for models with a language modeling head using **greedy decoding** or
-        **sample** (depending on `do_sample`), assisted by a smaller model. Can be used for text-decoder, text-to-text,
-        speech-to-text, and vision-to-text models.
+        **sample** (depending on `do_sample`), assisted by candidate sequences. Assisted generation is an example of a
+        candidate decoding strategy. Can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text
+        models.
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.assisted_decoding`] directly. Use
+        In most cases, you do not need to call [`~generation.GenerationMixin.candidate_decoding`] directly. Use
         generate() instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -4409,6 +4424,9 @@ def assisted_decoding(
         Parameters:
             input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                 The sequence used as a prompt for the generation.
+            candidate_generator (`CandidateGenerator`, *optional*):
+                A derived instance of [`CandidateGenerator`] that defines how candidate sequences are generated. For
+                more information, the documentation of [`CandidateGenerator`] should be read. Only one of `assistant_model` or `candidate_generator` should be passed as input to this function.
             assistant_model (`PreTrainedModel`, *optional*):
                 An assistant model that can be used to accelerate generation. The assistant model must have the exact
                 same tokenizer. The acceleration is achieved when forecasting candidate tokens with the assistent model
@@ -4491,15 +4509,23 @@ def assisted_decoding(
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
         ["It might be possible to get a better understanding of the nature of the problem, but it's not"]
         ```"""
-        # Assistant: initialize assistant-related variables
-        if hasattr(assistant_model, "num_assistant_tokens"):
+        # handling deprecated arguments
+        if (assistant_model is None) == (candidate_generator is None):
+            raise ValueError("One (and only one) of `assistant_model` and `candidate_generator` should be defined.")
+
+        if assistant_model is not None:
+            candidate_generator = AssistedCandidateGenerator(
+                input_ids=input_ids,
+                assistant_model=assistant_model,
+                logits_processor=logits_processor,
+                model_kwargs=model_kwargs,
+                eos_token_id=eos_token_id,
+            )
             warnings.warn(
-                "Setting `num_assistant_tokens` via `assistant_model.num_assistant_tokens` is deprecated and will be removed in v.37. Make sure to set `num_assistant_tokens` via the generation_config instead.",
+                "Passing `assistant_model` to `assisted_decoding` is deprecated and will be removed in v4.38. "
+                "Pass the `candidate_generator` argument instead.",
                 FutureWarning,
             )
-            num_assistant_tokens = assistant_model.num_assistant_tokens
-        else:
-            num_assistant_tokens = assistant_model.generation_config.num_assistant_tokens
 
         # init values
         logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
@@ -4538,27 +4564,6 @@ def assisted_decoding(
                 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
             )
 
-        # prepare assistant model's keys of inputs
-        assistant_kwargs = copy.copy(model_kwargs)
-        if assistant_model.config.is_encoder_decoder:
-            # both are encoder-decoder
-            input_ids_key = "decoder_input_ids"
-            attention_key = "decoder_attention_mask"
-            assistant_kwargs["encoder_outputs"] = assistant_kwargs.pop("assistant_encoder_outputs")
-        elif "assistant_encoder_outputs" in assistant_kwargs:
-            # special case for encoder-decoder with decoder-only assistant (like DistilWhisper)
-            input_ids_key = "input_ids"
-            attention_key = "attention_mask"
-            assistant_kwargs["attention_mask"] = assistant_kwargs.get(
-                "decoder_attention_mask",
-                torch.ones((input_ids.shape[0], 1), device=input_ids.device, dtype=torch.long),
-            )
-            assistant_kwargs["encoder_outputs"] = assistant_kwargs.pop("assistant_encoder_outputs")
-        else:
-            # both are decoder-only
-            input_ids_key = "input_ids"
-            attention_key = "attention_mask"
-
         # keep track of which sequences are already finished
         unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
 
@@ -4577,54 +4582,18 @@ def assisted_decoding(
                 if this_peer_finished_flag.item() == 0.0:
                     break
 
-            # Assistant: main logic start
             cur_len = input_ids.shape[-1]
 
-            #  1. Forecast next N tokens using the assistant model. This `for` block can be replaced with a
-            # `.generate()` call if we decide to add `past_key_values` as a possible output of generate, as we
-            # need access to the assistant cache to secure strong speedups.
-            candidate_input_ids = input_ids
-            for _ in range(int(num_assistant_tokens)):
-                # 1.1 prepare assistant model inputs
-                assistant_inputs = assistant_model.prepare_inputs_for_generation(
-                    candidate_input_ids,
-                    **assistant_kwargs,
-                )
-
-                # 1.2. check if the input ids length is correct
-                has_past_key_values = assistant_inputs.get("past_key_values", None) is not None
-                if has_past_key_values and assistant_inputs[input_ids_key].shape[-1] not in (1, 2):
-                    raise ValueError("The length of the input ids in assistant inputs should be 1 or 2")
-
-                # 1.3. use the assistant model to obtain the next candidate logits
-                assistant_model_outputs = assistant_model(**assistant_inputs)
-
-                # 1.4. greedily select the next candidate token
-                if len(logits_processor) > 0:
-                    assistant_model_outputs.logits[:, -1, :] = logits_processor(
-                        candidate_input_ids, assistant_model_outputs.logits[:, -1, :]
-                    )
-                new_token = assistant_model_outputs.logits[:, -1, :].argmax(dim=-1)
-                candidate_input_ids = torch.cat((candidate_input_ids, new_token[:, None]), dim=-1)
-
-                # 1.5. update assistant model inputs
-                if assistant_kwargs.get(attention_key, None) is not None:
-                    mask = assistant_kwargs[attention_key]
-                    assistant_kwargs[attention_key] = torch.cat([mask, mask.new_ones((mask.shape[0], 1))], dim=-1)
-                assistant_kwargs["past_key_values"] = assistant_model_outputs.past_key_values
-
-                # 1.6. stop assistant generation on EOS
-                if eos_token_id_tensor is not None:
-                    last_assistant_token_is_eos = new_token.tile(eos_token_id_tensor.shape[0], 1)
-                    last_assistant_token_is_eos = (
-                        ~last_assistant_token_is_eos.ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0).bool()
-                    )
-                    if last_assistant_token_is_eos:
-                        break
-                else:
-                    last_assistant_token_is_eos = False
-
+            #  1. Fetch candidate sequences from a `CandidateGenerator`
+            candidate_input_ids = candidate_generator.get_candidates(input_ids)
             candidate_length = candidate_input_ids.shape[1] - input_ids.shape[1]
+            last_assistant_token_is_eos = (
+                ~candidate_input_ids[:, -1]
+                .tile(eos_token_id_tensor.shape[0], 1)
+                .ne(eos_token_id_tensor.unsqueeze(1))
+                .prod(dim=0)
+                .bool()
+            )
 
             # 2. Use the original model to obtain the next token logits given the candidate sequence. We obtain
             # `candidate_length + 1` relevant logits from this process: in the event that all candidates are correct,
@@ -4687,20 +4656,10 @@ def assisted_decoding(
             # 5.3. Discard past key values relative to unused assistant tokens
             new_cache_size = new_cur_len - 1
             outputs.past_key_values = _crop_past_key_values(self, outputs.past_key_values, new_cache_size)
-            assistant_kwargs["past_key_values"] = _crop_past_key_values(
-                assistant_model, assistant_kwargs["past_key_values"], new_cache_size - 1
-            )  # the assistant does not have the token after the last match, hence the -1
-
-            # 6. Adjust the max number of assistant tokens to use in the next iteration. This is a simple heuristic,
-            # probably can be improved -- we want to balance the benefits of getting assistant tokens correct with the
-            # cost of forecasting incorrect assistant tokens.
-            if assistant_model.generation_config.num_assistant_tokens_schedule == "heuristic":
-                if n_matches == int(num_assistant_tokens):
-                    num_assistant_tokens += 2.0
-                else:
-                    num_assistant_tokens = max(1.0, num_assistant_tokens - 1.0)
 
-            # Assistant: main logic end
+            # 6. Update the candidate generation strategy if needed
+            candidate_generator.update_candidate_strategy(input_ids, new_logits, n_matches)
+
             if synced_gpus and this_peer_finished:
                 continue  # don't waste resources running the code we don't need
 
@@ -4749,12 +4708,6 @@ def assisted_decoding(
                 outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
             )
 
-            # Update assistant_kwargs for the assistant's next round of generations
-            assistant_kwargs = _prepare_attention_mask(
-                assistant_kwargs, new_cur_len, assistant_model.config.is_encoder_decoder
-            )
-            assistant_kwargs = _prepare_token_type_ids(assistant_kwargs, new_cur_len)
-
             # if eos_token was found in one sentence, set sentence to finished
             if eos_token_id_tensor is not None:
                 unfinished_sequences = unfinished_sequences.mul(
@@ -4802,54 +4755,6 @@ def assisted_decoding(
             return input_ids
 
 
-def _crop_past_key_values(model, past_key_values, maximum_length):
-    """Crops the past key values up to a certain maximum length."""
-    new_past = []
-    if model.config.is_encoder_decoder:
-        for idx in range(len(past_key_values)):
-            new_past.append(
-                (
-                    past_key_values[idx][0][:, :, :maximum_length, :],
-                    past_key_values[idx][1][:, :, :maximum_length, :],
-                    past_key_values[idx][2],
-                    past_key_values[idx][3],
-                )
-            )
-        past_key_values = tuple(new_past)
-    # bloom is special
-    elif "bloom" in model.__class__.__name__.lower() or (
-        model.config.architectures is not None and "bloom" in model.config.architectures[0].lower()
-    ):
-        for idx in range(len(past_key_values)):
-            new_past.append(
-                (
-                    past_key_values[idx][0][:, :, :maximum_length],
-                    past_key_values[idx][1][:, :maximum_length, :],
-                )
-            )
-        past_key_values = tuple(new_past)
-    # gptbigcode is too
-    elif "gptbigcode" in model.__class__.__name__.lower() or (
-        model.config.architectures is not None and "gptbigcode" in model.config.architectures[0].lower()
-    ):
-        if model.config.multi_query:
-            for idx in range(len(past_key_values)):
-                past_key_values[idx] = past_key_values[idx][:, :maximum_length, :]
-        else:
-            for idx in range(len(past_key_values)):
-                past_key_values[idx] = past_key_values[idx][:, :, :maximum_length, :]
-    else:
-        for idx in range(len(past_key_values)):
-            new_past.append(
-                (
-                    past_key_values[idx][0][:, :, :maximum_length, :],
-                    past_key_values[idx][1][:, :, :maximum_length, :],
-                )
-            )
-        past_key_values = tuple(new_past)
-    return past_key_values
-
-
 def _split_model_outputs(outputs, new_outputs, cur_len, added_len, is_decoder_attention=False):
     """
     Given the (decoder/cross attentions)/(decoder hidden states) for multiple generated tokens, splits it into a tuple
@@ -4932,37 +4837,3 @@ def _ranking_fast(
     contrastive_score = torch.stack(torch.split(contrastive_score, beam_width))  # [B, K]
     _, selected_idx = contrastive_score.max(dim=-1)  # [B]
     return selected_idx
-
-
-def _prepare_attention_mask(model_kwargs: Dict[str, Any], new_length: int, is_encoder_decoder: bool) -> Dict[str, Any]:
-    """Expands or crops the model's mask for decoding purposes, to the defined length"""
-
-    mask_key = "decoder_attention_mask" if is_encoder_decoder else "attention_mask"
-    if mask_key not in model_kwargs:
-        return model_kwargs
-
-    mask = model_kwargs[mask_key]
-    mask_length_diff = new_length - mask.shape[1]
-
-    if mask_length_diff < 0:
-        model_kwargs[mask_key] = mask[:, :mask_length_diff]
-    elif mask_length_diff > 0:
-        model_kwargs[mask_key] = torch.cat([mask, mask.new_ones((mask.shape[0], mask_length_diff))], dim=-1)
-    return model_kwargs
-
-
-def _prepare_token_type_ids(model_kwargs: Dict[str, Any], new_length: int) -> Dict[str, Any]:
-    """Expands or crops the model's token_type_ids for decoding purposes, to the defined length"""
-    if "token_type_ids" not in model_kwargs or model_kwargs["token_type_ids"] is None:
-        return model_kwargs
-
-    token_type_ids = model_kwargs["token_type_ids"]
-    final_token_type = token_type_ids[:, -1].unsqueeze(-1)
-    type_length_diff = new_length - token_type_ids.shape[1]
-
-    if type_length_diff < 0:
-        token_type_ids = token_type_ids[:, :type_length_diff]
-    elif type_length_diff > 0:
-        token_type_copies = final_token_type.repeat(1, type_length_diff)
-        model_kwargs["token_type_ids"] = torch.cat([model_kwargs["token_type_ids"], token_type_copies], dim=-1)
-    return model_kwargs