diff --git a/docs/source/en/generation_strategies.md b/docs/source/en/generation_strategies.md
index b000cc06779918..68430de643f17b 100644
--- a/docs/source/en/generation_strategies.md
+++ b/docs/source/en/generation_strategies.md
@@ -178,7 +178,7 @@ An increasing sequence: one, two, three, four, five, six, seven, eight, nine, te
 
 The `generate()` method supports caching keys and values to enhance efficiency and avoid re-computations. However the key and value
 cache can occupy a large portion of memory, becoming a bottleneck for long-context generation, especially for Large Language Models.
-Quantizing the cache when using `generate()` can significantly reduce memory requirements at the cost of speed. 
+Quantizing the cache when using `generate()` can significantly reduce memory requirements at the cost of speed.
 
 KV Cache quantization in `transformers` is largely inspired by the paper [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache]
 (https://arxiv.org/abs/2402.02750) and currently supports `quanto` and `HQQ` as backends. For more information on the inner workings see the paper.
@@ -213,11 +213,11 @@ I like rock music because it's loud and energetic. I like to listen to it when I
 
 ## Watermarking
 
-The `generate()` supports watermarking the generated text by randomly marking a portion of tokens as "green". 
+The `generate()` supports watermarking the generated text by randomly marking a portion of tokens as "green".
 When generating the "green" will have a small 'bias' value added to their logits, thus having a higher chance to be generated.
 The watermarked text can be detected by calculating the proportion of "green" tokens in the text and estimating how likely it is
-statistically to obtain that amount of "green" tokens for human-generated text. This watermarking strategy was proposed in the paper 
-["On the Reliability of Watermarks for Large Language Models"](https://arxiv.org/abs/2306.04634). For more information on 
+statistically to obtain that amount of "green" tokens for human-generated text. This watermarking strategy was proposed in the paper
+["On the Reliability of Watermarks for Large Language Models"](https://arxiv.org/abs/2306.04634). For more information on
 the inner functioning of watermarking, it is recommended to refer to the paper.
 
 The watermarking can be used with any generative model in `tranformers` and does not require an extra classification model
@@ -484,3 +484,59 @@ just like in multinomial sampling. However, in assisted decoding, reducing the t
 
 Alternativelly, you can also set the `prompt_lookup_num_tokens` to trigger n-gram based assisted decoding, as opposed
 to model based assisted decoding. You can read more about it [here](https://twitter.com/joao_gante/status/1747322413006643259).
+### DoLa Decoding
+
+**D**ecoding by C**o**ntrasting **La**yers (DoLa) is a contrastive decoding strategy to improve the factuality and reduce the
+hallucinations of LLMs, as described in this paper of ICLR 2024 [DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models](https://arxiv.org/abs/2309.03883).
+
+DoLa is achieved by contrasting the differences in logits obtained from final
+layers versus earlier layers, thus amplify the factual knowledge localized to particular part of transformer layers.
+
+Do the following two steps to activate DoLa decoding when calling the `model.generate` function:
+1. Set the `dola_layers` argument, which can be either a string or a list of integers.
+    - If set to a string, it can be one of `low`, `high`.
+    - If set to a list of integers, it should be a list of layer indices between 0 and the total number of layers in the model. The 0-th layer is word embedding, and the 1st layer is the first transformer layer, and so on.
+2. Set `repetition_penalty = 1.2` is suggested to reduce repetition in DoLa decoding.
+
+See the following examples for DoLa decoding with the 32-layer LLaMA-7B model.
+
+```python
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+>>> import torch
+
+>>> tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
+>>> model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", torch_dtype=torch.float16)
+>>> device = 'cuda' if torch.cuda.is_available() else 'cpu'
+>>> model.to(device)
+>>> set_seed(42)
+
+>>> text = "On what date was the Declaration of Independence officially signed?"
+>>> inputs = tokenizer(text, return_tensors="pt").to(device)
+
+# Vanilla greddy decoding
+>>> vanilla_output = model.generate(**inputs, do_sample=False, max_new_tokens=50)
+>>> tokenizer.batch_decode(vanilla_output[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+['\nThe Declaration of Independence was signed on July 4, 1776.\nWhat was the date of the signing of the Declaration of Independence?\nThe Declaration of Independence was signed on July 4,']
+
+# DoLa decoding with contrasting higher part of layers (layers 16,18,...,30)
+>>> dola_high_output = model.generate(**inputs, do_sample=False, max_new_tokens=50, dola_layers='high')
+>>> tokenizer.batch_decode(dola_high_output[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+['\nJuly 4, 1776, when the Continental Congress voted to separate from Great Britain. The 56 delegates to the Continental Congress signed the Declaration on August 2, 1776.']
+
+# DoLa decoding with contrasting specific layers (layers 28 and 30)
+>>> dola_custom_output = model.generate(**inputs, do_sample=False, max_new_tokens=50, dola_layers=[28,30], repetition_penalty=1.2)
+>>> tokenizer.batch_decode(dola_custom_output[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+['\nIt was officially signed on 2 August 1776, when 56 members of the Second Continental Congress, representing the original 13 American colonies, voted unanimously for the resolution for independence. The 2']
+```
+
+#### Understanding the `dola_layers` argument
+
+`dola_layers` stands for the candidate layers in premature layer selection, as described in the DoLa paper. The selected premature layer will be contrasted with the final layer.
+
+Setting `dola_layers` to `'low'` or `'high'` will select the lower or higher part of the layers to contrast, respectively.
+- For `N`-layer models with `N <= 40` layers, the layers of `range(0, N // 2, 2)` and `range(N // 2, N, 2)` are used for `'low'` and `'high'` layers, respectively.
+- For models with `N > 40` layers, the layers of `range(0, 20, 2)` and `range(N - 20, N, 2)` are used for `'low'` and `'high'` layers, respectively.
+- If the model has tied word embeddings, we skip the word embeddings (0-th) layer and start from the 2nd layer, as the early exit from word embeddings will become identity function.
+- Set the `dola_layers` to a list of integers for layer indices to contrast manually specified layers. For example, setting `dola_layers=[28,30]` will contrast the final layer (32-th layer) with the 28-th and 30-th layers.
+
+The paper suggested that contrasting `'high'` layers to improve short-answer tasks like TruthfulQA, and contrasting `'low'` layers to improve all the other long-answer reasoning tasks, such as GSM8K, StrategyQA, FACTOR, and VicunaQA. Applying DoLa to smaller models like GPT-2 is not recommended, as the results shown in the Appendix N of the paper.
diff --git a/src/transformers/generation/configuration_utils.py b/src/transformers/generation/configuration_utils.py
index 8ba17a6a350f78..dcdccad23a54c1 100644
--- a/src/transformers/generation/configuration_utils.py
+++ b/src/transformers/generation/configuration_utils.py
@@ -60,6 +60,7 @@ class GenerationMode(ExplicitEnum):
     GREEDY_SEARCH = "greedy_search"
     SAMPLE = "sample"
     ASSISTED_GENERATION = "assisted_generation"
+    DOLA_GENERATION = "dola_generation"
     # Beam methods
     BEAM_SEARCH = "beam_search"
     BEAM_SAMPLE = "beam_sample"
@@ -81,6 +82,7 @@ class GenerationConfig(PushToHubMixin):
         - *diverse beam-search decoding* if `num_beams>1` and `num_beam_groups>1`
         - *constrained beam-search decoding* if `constraints!=None` or `force_words_ids!=None`
         - *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`
+        - *dola decoding* if `dola_layers` is passed to `.generate()`
 
     To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
 
@@ -305,6 +307,18 @@ class GenerationConfig(PushToHubMixin):
         max_matching_ngram_size (`int`, *optional*, default to `None`):
             The maximum ngram size to be considered for matching in the prompt. Default to 2 if not provided.
 
+        > Generation parameters exclusive to [DoLa decoding](https://arxiv.org/abs/2309.03883)
+
+        dola_layers (`str` or `List[int]`, *optional*):
+            The layers to use for DoLa decoding. If `None`, DoLa decoding is not used. If a string, it must
+            be one of "low" or "high", which means using the lower part or higher part of the model layers, respectively.
+            "low" means the first half of the layers up to the first 20 layers, and "high" means the last half of the
+            layers up to the last 20 layers.
+            If a list of integers, it must contain the indices of the layers to use for candidate premature layers in DoLa.
+            The 0-th layer is the word embedding layer of the model. Set to `'low'` to improve long-answer reasoning tasks,
+            `'high'` to improve short-answer tasks. Check the [documentation](https://github.com/huggingface/transformers/blob/main/docs/source/en/generation_strategies.md)
+            or [the paper](https://arxiv.org/abs/2309.03883) for more details.
+
         > Parameters specific to the caching mechanism:
 
         cache_implementation (`str`, *optional*, default to `None`):
@@ -397,6 +411,9 @@ def __init__(self, **kwargs):
         self.num_assistant_tokens = kwargs.pop("num_assistant_tokens", 5)
         self.num_assistant_tokens_schedule = kwargs.pop("num_assistant_tokens_schedule", "heuristic")
 
+        # DoLa generation
+        self.dola_layers = kwargs.pop("dola_layers", None)
+
         # Cache implementation
         self.cache_implementation = kwargs.pop("cache_implementation", None)
         self.cache_config = kwargs.pop("cache_config", None)
@@ -495,6 +512,16 @@ def get_generation_mode(self, assistant_model: Optional["PreTrainedModel"] = Non
                     "You've set `assistant_model`, which triggers assisted generate. Currently, assisted generate "
                     "is only supported with Greedy Search and Sample."
                 )
+
+        # DoLa generation may extend some generation modes
+        if self.dola_layers is not None:
+            if generation_mode in ("greedy_search", "sample"):
+                generation_mode = GenerationMode.DOLA_GENERATION
+            else:
+                raise ValueError(
+                    "You've set `dola_layers`, which triggers DoLa generate. Currently, DoLa generate "
+                    "is only supported with Greedy Search and Sample."
+                )
         return generation_mode
 
     def validate(self, is_init=False):
@@ -700,6 +727,17 @@ def validate(self, is_init=False):
                     "`generate()` (or a pipeline) directly."
                 )
 
+        # 6. if dola_layers is set, check if repetition_penalty is set to >= 1.2
+        if self.dola_layers is not None and (self.repetition_penalty is None or self.repetition_penalty < 1.2):
+            dola_decoding_wrong_parameter_msg = (
+                "`dola_layers` is set to trigger DoLa decoding, but `repetition_penalty` is set to a value of {repetition_penalty}, "
+                "which could induce unwanted repetition. The recommended value for DoLa decoding is `repetition_penalty>=1.2`."
+            )
+            warnings.warn(
+                dola_decoding_wrong_parameter_msg.format(repetition_penalty=self.repetition_penalty),
+                UserWarning,
+            )
+
     def save_pretrained(
         self,
         save_directory: Union[str, os.PathLike],
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 371c59460309b3..6b4a055fba8de3 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -20,9 +20,11 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
+import numpy as np
 import torch
 import torch.distributed as dist
 from torch import nn
+from torch.nn import functional as F
 
 from ..cache_utils import (
     Cache,
@@ -1908,6 +1910,28 @@ def generate(
                 streamer=streamer,
                 **model_kwargs,
             )
+        elif generation_mode == GenerationMode.DOLA_GENERATION:
+            if self._is_stateful:
+                # DoLa decoding was not designed for stateful models, and would require some changes
+                raise ValueError(
+                    f"dola decoding is not supported with stateful models, such as {self.__class__.__name__}"
+                )
+            prepared_logits_warper = (
+                self._get_logits_warper(generation_config, device=input_ids.device)
+                if generation_config.do_sample
+                else None
+            )
+            result = self._dola_decoding(
+                input_ids,
+                dola_layers=generation_config.dola_layers,
+                logits_processor=prepared_logits_processor,
+                logits_warper=prepared_logits_warper,
+                stopping_criteria=prepared_stopping_criteria,
+                generation_config=generation_config,
+                synced_gpus=synced_gpus,
+                streamer=streamer,
+                **model_kwargs,
+            )
 
         elif generation_mode == GenerationMode.CONTRASTIVE_SEARCH:
             if not model_kwargs["use_cache"]:
@@ -2189,6 +2213,225 @@ def contrastive_search(self, *args, **kwargs):
         )
         return self._contrastive_search(*args, **kwargs)
 
+    def _dola_decoding(
+        self,
+        input_ids: torch.LongTensor,
+        dola_layers: Union[str, List[int]],
+        logits_processor: LogitsProcessorList,
+        stopping_criteria: StoppingCriteriaList,
+        generation_config: GenerationConfig,
+        synced_gpus: bool,
+        streamer: Optional["BaseStreamer"] = None,
+        logits_warper: Optional[LogitsProcessorList] = None,
+        **model_kwargs,
+    ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **dola decoding** and can be
+        used for decoder-only text models.
+        The method is based on the paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language
+        Models" (https://arxiv.org/abs/2309.03883) in ICLR 2024.
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            dola_layers (`Union[str, List[int]]`):
+                The candidate layers used in contrasting layers of DoLa. It can be either 1) 'low' or 'high', which
+                means the lower part or higher part of the model layers, respectively, or 2) a list of layer indices
+                to be used for candidate layers. The 0-th layer is the word embedding layer of the 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.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            generation_config ([`~generation.GenerationConfig`]):
+                The generation configuration to be used as parametrization of the decoding method.
+            synced_gpus (`bool`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            streamer (`BaseStreamer`, *optional*):
+                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+            logits_warper (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
+                to warp the prediction score distribution of the language modeling head applied before multinomial
+                sampling at each generation step.
+            model_kwargs:
+                Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
+                If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`]
+            or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+        """
+
+        if self.config.is_encoder_decoder:
+            raise ValueError("DoLa decoding is only available for decoder-only models.")
+        # init values
+
+        pad_token_id = generation_config.pad_token_id
+        output_attentions = generation_config.output_attentions
+        output_hidden_states = generation_config.output_hidden_states
+        output_scores = generation_config.output_scores
+        output_logits = generation_config.output_logits
+        return_dict_in_generate = generation_config.return_dict_in_generate
+        has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
+        do_sample = generation_config.do_sample
+        if do_sample is True and not isinstance(logits_warper, LogitsProcessorList):
+            raise ValueError(
+                "`do_sample` is set to `True`, `logits_warper` must be a `LogitsProcessorList` instance (it is "
+                f"{logits_warper})."
+            )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        raw_logits = () if (return_dict_in_generate and output_logits) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # keep track of which sequences are already finished
+        batch_size = input_ids.shape[0]
+        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+        model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
+
+        this_peer_finished = False
+
+        # prepare layers for DoLa decoding
+        final_layer = self.config.num_hidden_layers
+        # if the model has tied word embeddings, we skip the word embeddings (0-th) layer and start from the 2nd layer,
+        # as the early exit from word embeddings will become identity function
+        # if the model is really shallow (<=2 layers), we use the 1st layer if it's not the final layer and the 0-th
+        # layer otherwise. Notice that DoLa does not help shallow models much.
+        if not self.config.tie_word_embeddings:
+            start_layer = 0
+        elif final_layer > 2:
+            start_layer = 2
+        elif final_layer == 2:
+            start_layer = 1
+        else:
+            start_layer = 0
+
+        # For `N`-layer models with `N <= 40` layers, the layers of `range(0, N // 2, 2)` and `range(N // 2, N, 2)`
+        # are used for `'low'` and `'high'` layers, respectively.
+        # For models with `N > 40` layers, the layers of `range(0, 20, 2)` and `range(N - 20, N, 2)` are used for
+        # `'low'` and `'high'` layers, respectively.
+        if isinstance(dola_layers, str) and dola_layers == "low":
+            if start_layer == final_layer // 2:
+                candidate_premature_layers = [start_layer]
+            else:
+                candidate_premature_layers = (
+                    list(range(start_layer, final_layer // 2, 2))
+                    if final_layer <= 40
+                    else list(range(start_layer, 20, 2))
+                )
+        elif isinstance(dola_layers, str) and dola_layers == "high":
+            candidate_premature_layers = (
+                list(range(final_layer // 2, final_layer, 2))
+                if final_layer <= 40
+                else list(range(final_layer - 20, final_layer, 2))
+            )
+        # Set the `dola_layers` to a list of integers for layer indices to contrast manually specified layers.
+        elif isinstance(dola_layers, list):
+            candidate_premature_layers = [i for i in dola_layers if i < final_layer]
+        else:
+            raise ValueError("dola_layers must be either 'low', 'high' or a list of integers.")
+
+        lm_head = self.get_output_embeddings()
+        if lm_head is None:
+            raise ValueError("DoLa is not supported for models that don't have output embeddings.")
+
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
+            # prepare model inputs
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            # forward pass to get next token
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=True,
+            )
+
+            final_layer_next_token_logits = outputs.logits[:, -1, :].detach().clone()
+            final_logits = outputs.logits[:, -1, :]
+            candidate_premature_logits = {}
+            for candidate_premature_layer in candidate_premature_layers:
+                candidate_premature_logits[candidate_premature_layer] = lm_head(
+                    outputs.hidden_states[candidate_premature_layer][:, -1, :]
+                )
+
+            if synced_gpus and this_peer_finished:
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = _dola_select_contrast(
+                candidate_premature_layers, candidate_premature_logits, final_logits
+            )
+            # pre-process distribution
+            next_token_scores = logits_processor(input_ids, next_token_logits)
+            if do_sample:  # sample
+                next_token_scores = logits_warper(input_ids, next_token_scores)
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_token_scores,)
+                if output_logits:
+                    raw_logits += (final_layer_next_token_logits,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            if do_sample:  # sample
+                probs = nn.functional.softmax(next_token_scores, dim=-1)
+                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+            else:  # argmax
+                next_tokens = torch.argmax(next_token_scores, dim=-1)
+
+            # finished sentences should have their next token be a padding token
+            if has_eos_stopping_criteria:
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
+
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            if streamer is not None:
+                streamer.put(next_tokens.cpu())
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs,
+                model_kwargs,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+            )
+
+            # stop when each sentence is finished
+            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
+            this_peer_finished = unfinished_sequences.max() == 0
+
+        if streamer is not None:
+            streamer.end()
+
+        if return_dict_in_generate:
+            return GenerateDecoderOnlyOutput(
+                sequences=input_ids,
+                scores=scores,
+                logits=raw_logits,
+                attentions=decoder_attentions,
+                hidden_states=decoder_hidden_states,
+                past_key_values=model_kwargs.get("past_key_values"),
+            )
+        else:
+            return input_ids
+
     @torch.no_grad()
     def _contrastive_search(
         self,
@@ -4197,3 +4440,75 @@ def _concat(data):
 
     # Return a new object of the inferred class with the concatenated attributes
     return model_output_cls(**concatenated_data)
+
+
+def _relative_top_filter(
+    scores: torch.FloatTensor,
+    baseline_scores: torch.FloatTensor,
+    relative_top: float = 0.1,
+    filter_value: float = -float("Inf"),
+    base_filter_value=-1e-3,
+    min_tokens_to_keep: int = 1,
+) -> torch.FloatTensor:
+    """
+    Reference: https://github.com/XiangLi1999/ContrastiveDecoding/blob/170e9142e92159c1237d731e240f5eb14aabf428/transformers/src/transformers/generation_logits_process.py#L235
+    Apply filtering to only keep tokens with a probability above a certain threshold. The threshold is defined as `relative_top` * max probability in the distribution.
+    """
+    scores_normalized = scores.log_softmax(dim=-1)
+    baseline_scores_normalized = baseline_scores.log_softmax(dim=-1)
+    sorted_logits, sorted_indices = torch.sort(scores_normalized, descending=True)
+    min_thresh = sorted_logits[..., min_tokens_to_keep - 1]
+    probs_max = torch.max(scores_normalized, dim=-1).values
+    probs_thresh = probs_max + np.log(relative_top)
+    probs_thresh = torch.min(min_thresh, probs_thresh)
+    probs_thresh = probs_thresh.unsqueeze(-1)
+    baseline_scores_normalized[scores_normalized < probs_thresh] = base_filter_value
+    scores_normalized[scores_normalized < probs_thresh] = filter_value
+    return scores_normalized, baseline_scores_normalized
+
+
+def _dola_select_contrast(
+    candidate_premature_layers: List[int],
+    candidate_premature_logits: Dict[int, torch.FloatTensor],
+    final_logits: torch.FloatTensor,
+) -> torch.FloatTensor:
+    if len(candidate_premature_layers) == 1:
+        base_logits = candidate_premature_logits[candidate_premature_layers[0]]
+        final_logits, base_logits = _relative_top_filter(final_logits, base_logits)
+        logits = final_logits - base_logits
+        return logits
+
+    # 1. Stacking all premature_layers into a new dimension
+    stacked_premature_layers = torch.stack([candidate_premature_logits[i] for i in candidate_premature_layers], dim=0)
+
+    # 2. Calculate the softmax values for mature_layer and all premature_layers
+    # shape: (batch_size, vocab_size)
+    softmax_mature_layer = F.softmax(final_logits, dim=-1)
+    # shape: (num_premature_layers, batch_size, vocab_size)
+    softmax_premature_layers = F.softmax(stacked_premature_layers, dim=-1)
+
+    # 3. Calculate the average distribution
+    # shape: (num_premature_layers, batch_size, vocab_size)
+    avg_dist = 0.5 * (softmax_mature_layer[None, :, :] + softmax_premature_layers)
+
+    # 4. Calculate log-softmax for the KL divergence
+    # shape: (batch_size, vocab_size)
+    log_softmax_mature_layer = F.log_softmax(final_logits, dim=-1)
+    # shape: (num_premature_layers, batch_size, vocab_size)
+    log_softmax_premature_layers = F.log_softmax(stacked_premature_layers, dim=-1)
+
+    # 5. Calculate the KL divergences and then the JS divergences
+    # shape: (num_premature_layers, batch_size)
+    kl1 = F.kl_div(log_softmax_mature_layer[None, :, :], avg_dist, reduction="none").mean(-1)
+    # shape: (num_premature_layers, batch_size)
+    kl2 = F.kl_div(log_softmax_premature_layers, avg_dist, reduction="none").mean(-1)
+    js_divs = 0.5 * (kl1 + kl2)  # shape: (num_premature_layers, batch_size)
+
+    # 6. Reduce the batchmean
+    js_divs = js_divs.mean(-1)  # shape: (num_premature_layers,)
+    premature_layer = candidate_premature_layers[int(js_divs.argmax().cpu().item())]
+
+    base_logits = candidate_premature_logits[premature_layer]
+    final_logits, base_logits = _relative_top_filter(final_logits, base_logits)
+    logits = final_logits - base_logits
+    return logits
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 8fa41fbdbe2b07..cab6fe8d094cd6 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -1264,6 +1264,55 @@ def test_prompt_lookup_decoding_matches_greedy_search(self):
             for output in (output_greedy, output_prompt_lookup):
                 self._check_outputs(output, input_ids, model.config, use_cache=True)
 
+    def test_dola_decoding_sample(self):
+        # TODO (joao): investigate skips, try to reduce incompatibilities
+        for model_class in self.all_generative_model_classes:
+            if model_class._is_stateful:
+                self.skipTest(reason="Stateful models don't support DoLa decoding")
+
+            if any(model_name in model_class.__name__.lower() for model_name in ["reformer"]):
+                self.skipTest("Skip Reformer as the lm_head input size is 2 * hidden size, adopted from Rev Nets.")
+
+            if any(model_name in model_class.__name__.lower() for model_name in ["marian", "mbart", "pegasus"]):
+                self.skipTest("DoLa is not supported for models that don't return layerwise hidden states")
+
+            # enable cache if the model is not openai-gpt, xlnet, cpm, or xlm
+            config, input_ids, attention_mask = self._get_input_ids_and_config()
+
+            # Some models don't support the cache and returning past_key_values
+            if not hasattr(config, "use_cache"):
+                config.use_cache = False
+            else:
+                config.use_cache = True
+
+            # Encoder-decoder models are not supported
+            if config.is_encoder_decoder:
+                self.skipTest("DoLa is not supported for encoder-decoder models")
+            config.is_decoder = True
+            model = model_class(config).to(torch_device).eval()
+
+            if model.get_output_embeddings() is None:
+                self.skipTest("DoLa is not supported for models that don't have output embeddings")
+            # Sets dola generation arguments such that:
+            # a) no EOS is generated, to ensure generation doesn't break early
+            # b) there are at least two forward passes in the main model, to ensure the input preparation of
+            #    the main model is correct
+            generation_kwargs = {
+                "eos_token_id": -1,  # see a)
+                "max_new_tokens": 4,  # see b)
+                "num_beams": 1,
+                "do_sample": True,
+                "output_scores": True,
+                "output_logits": True,
+                "output_hidden_states": True,
+                "output_attentions": self.has_attentions,
+                "return_dict_in_generate": True,
+            }
+            generation_kwargs.update({"dola_layers": "low"})
+            model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
+            output_dola = model.generate(input_ids, **model_kwargs, **generation_kwargs)
+            self._check_outputs(output_dola, input_ids, model.config, use_cache=config.use_cache)
+
     def test_assisted_decoding_sample(self):
         # In this test we don't check assisted vs non-assisted output -- seeded assisted decoding with sample will not
         # match sample for the same seed, as the forward pass does not return the exact same logits (due to matmul with
diff --git a/tests/models/gemma/test_modeling_gemma.py b/tests/models/gemma/test_modeling_gemma.py
index e6a40eb10255a5..373885f535bd73 100644
--- a/tests/models/gemma/test_modeling_gemma.py
+++ b/tests/models/gemma/test_modeling_gemma.py
@@ -839,7 +839,6 @@ def test_model_7b_4bit(self):
 
         output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
         output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
-
         self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
 
     @slow
@@ -898,3 +897,24 @@ def test_compile_static_cache(self):
         )
         static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
         self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], static_compiled_text)
+
+    def test_model_2b_bf16_dola(self):
+        model_id = "google/gemma-2b"
+        # ground truth text generated with dola_layers="low", repetition_penalty=1.2
+        EXPECTED_TEXTS = [
+            "Hello I am doing an experiment and need to get the mass of a block. The problem is, it has no scale",
+            "Hi today we have the review for a <strong>2016/2017</strong> season of",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(
+            **inputs, max_new_tokens=20, do_sample=False, dola_layers="low", repetition_penalty=1.2
+        )
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(output_text, EXPECTED_TEXTS)
diff --git a/tests/models/llama/test_modeling_llama.py b/tests/models/llama/test_modeling_llama.py
index 0935e802c685b9..3e1d5c26eb1587 100644
--- a/tests/models/llama/test_modeling_llama.py
+++ b/tests/models/llama/test_modeling_llama.py
@@ -703,6 +703,29 @@ def test_model_7b_logits(self):
             )
         )
 
+    @slow
+    def test_model_7b_dola_generation(self):
+        # ground truth text generated with dola_layers="low", repetition_penalty=1.2
+        EXPECTED_TEXT_COMPLETION = (
+            "Simply put, the theory of relativity states that 1) time and space are relative, and 2) the laws of "
+            "physics are the same for all observers in uniform motion relative to one another.\n\nThe theory of "
+            "relativity was developed by Albert Einstein in the early 20th century, and it revolutionized our "
+            "understanding of space and time."
+        )
+        prompt = "Simply put, the theory of relativity states that "
+        tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+        model = LlamaForCausalLM.from_pretrained(
+            "meta-llama/Llama-2-7b-chat-hf", device_map="sequential", torch_dtype=torch.float16
+        )
+        model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(
+            **model_inputs, max_new_tokens=64, top_p=None, temperature=1, do_sample=False, dola_layers="low"
+        )
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
     @slow
     @require_torch_gpu
     @require_read_token
diff --git a/tests/models/mistral/test_modeling_mistral.py b/tests/models/mistral/test_modeling_mistral.py
index f2ba612a47b1c7..34246b8e967a82 100644
--- a/tests/models/mistral/test_modeling_mistral.py
+++ b/tests/models/mistral/test_modeling_mistral.py
@@ -555,6 +555,30 @@ def test_model_7b_generation(self):
         text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
         self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text)
 
+    @slow
+    def test_model_7b_dola_generation(self):
+        # ground truth text generated with dola_layers="low", repetition_penalty=1.2
+        EXPECTED_TEXT_COMPLETION = (
+            """My favourite condiment is 100% ketchup. I love it on everything, and I’m not ash"""
+        )
+        prompt = "My favourite condiment is "
+        tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
+        model = MistralForCausalLM.from_pretrained(
+            "mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16
+        )
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(
+            input_ids, max_new_tokens=20, temperature=0, dola_layers="low", repetition_penalty=1.2
+        )
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+        del model
+        backend_empty_cache(torch_device)
+        gc.collect()
+
     @require_bitsandbytes
     @slow
     @require_flash_attn