diff --git "a/modeling_mistral.py" "b/modeling_mistral.py"
new file mode 100644--- /dev/null
+++ "b/modeling_mistral.py"
@@ -0,0 +1,6934 @@
+# SpydazWeb AI MistralStar
+################################ Introduction ##############################
+
+# SpydazWeb AI Mistral Transformer ! this is a model based off of the mistral and mixtral models :
+# it is created t eneble the model to generate thoughts before generating response:
+# This is the first Generation of research;
+# this paradigm will be improved: - 
+
+## Note: to: Self:
+# the model should generate a thought based of the thought prompt , then it should use its thought generation to pass to the model input : 
+# with the original input : ( cross attention ) - 
+# this should enhance the input to the model also providing extra content for the generation stage:
+# ( later work ) - these thought should be generated by multiple heads : 
+# as perhaps internal agents/Experts hence for each head it would need head prompt :perhaps this should be a hardcoded process?
+# problem is how to frame it in the config ? - 
+# then each head could generate content and the final head suamarize the content with the input to provide a rich query?
+# in fact a single prompt is fine to hold multiple thoughts perhaps ,
+#  as this will be stacked on top of the input ? to the hidden context size may need to be larger than the model size?
+# PROJECT: ENDNING ?
+# we need to have the extra processor in the tokenizer or the model ( perhaps the tokenizer is the best place for input management , 
+# so to add the audio spectograph encoder and the Vision caption Trnsformer , 
+# so given a image or a sound it will provuide the outputs for each item prompt , 
+# hence the tokenizer response will need to be message based : ie seperate image description , seperate text ,
+# seperate audio description( not Speech as this shoudl be an other rag front end? or pre processor to the tokenizer ,
+#  for speech input it will handled in another model as that will be encoder/decoder ! this model is a decoder model and 
+# the tokenizer / preprocessors are the encoder layers ~!))
+
+
+
+
+
+################################ Imports ##############################
+import inspect
+import math
+import copy
+import os
+import time
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from termcolor import colored
+from tqdm import tqdm
+import random
+import numpy as np
+from matplotlib.colors import LinearSegmentedColormap, LogNorm
+import warnings
+from collections import defaultdict
+from typing import List, Optional, Tuple, Union
+
+
+from typing import List, Optional, Tuple, Union
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+import torch.nn.functional as F
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging,add_start_docstrings,add_start_docstrings_to_model_forward,replace_return_docstrings
+from transformers.modeling_utils import PreTrainedModel
+from transformers.cache_utils import Cache,DynamicCache, SlidingWindowCache, StaticCache
+from transformers.activations import ACT2FN
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import BaseModelOutputWithPast,CausalLMOutputWithPast,SequenceClassifierOutputWithPast,TokenClassifierOutput,QuestionAnsweringModelOutput,MoeCausalLMOutputWithPast,MoeModelOutputWithPast
+from tokenizers import processors
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import is_sentencepiece_available, logging
+from transformers.utils.versions import require_version
+from shutil import copyfile
+from collections import defaultdict
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+import sentencepiece as spm
+from transformers.convert_slow_tokenizer import import_protobuf
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
+from transformers.utils import logging
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+if TYPE_CHECKING:
+    from transformers.tokenization_utils_base import TextInput
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+MISTRAL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
+    "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
+}
+MISTRAL_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MistralConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+_CONFIG_FOR_DOC = "MistralStarConfig"
+logger = logging.get_logger(__name__)
+SPIECE_UNDERLINE = "▁"
+require_version("tokenizers>=0.13.3")
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+# fmt: off
+DEFAULT_SYSTEM_PROMPT = """you are the world archive of knowledge and a friendly helpful artificial intelligent being:
+consider if the current task denotes the creation of a function in python that can be saved and re-used for a future task, 
+if created, return the artifact to the user, in the response, or if access is given to be save as a file on the user system:
+Be flirtatious and joyfull, always willing to help or engage in conversation or express an opinion or sentiment: 
+answer all questions indepth supply and helpfull references where possible; """
+# fmt: on
+
+##############################   Models   #################################
+class MixtralConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an
+    Mixtral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Mixtral-7B-v0.1 or Mixtral-7B-Instruct-v0.1.
+
+    [mixtralai/Mixtral-8x7B](https://huggingface.co/mixtralai/Mixtral-8x7B)
+    [mixtralai/Mixtral-7B-Instruct-v0.1](https://huggingface.co/mixtralai/Mixtral-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Mixtral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MixtralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to route per-token, can be also interpreted as the `top-k` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabeling this will also
+            allow the model to output the auxiliary loss. See [here]() for more details
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+        router_jitter_noise (`float`, *optional*, defaults to 0.0):
+            Amount of noise to add to the router.
+
+    ```python
+    >>> from transformers import MixtralModel, MixtralConfig
+
+    >>> # Initializing a Mixtral 7B style configuration
+    >>> configuration = MixtralConfig()
+
+    >>> # Initializing a model from the Mixtral 7B style configuration
+    >>> model = MixtralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mixtral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=8,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        router_jitter_noise=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.router_jitter_noise = router_jitter_noise
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+class MistralStarConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
+    Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
+
+    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MistralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MistralModel, MistralConfig
+
+    >>> # Initializing a Mistral 7B style configuration
+    >>> configuration = MistralConfig()
+
+    >>> # Initializing a model from the Mistral 7B style configuration
+    >>> model = MistralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mistralstar"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        attention_dropout=0.0,
+        max_thoughts=16,
+        thought_length = 10,
+        merged_talk_heads=True,
+        merged_lm_and_talk_heads=False,
+        merged_lm_and_think_heads=True,
+        use_concat_talk_head=True,
+        use_shallow_think=True,
+        use_shallow_talk=False,
+        use_complex_think_head=False,
+        use_complex_talk_head=True,
+        use_weighted_talk_head=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.max_thoughts = max_thoughts
+        self.thought_length = thought_length
+        self.merged_talk_heads = merged_talk_heads
+        self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
+        self.merged_lm_and_think_heads = merged_lm_and_think_heads
+        self.use_concat_talk_head = use_concat_talk_head
+        self.use_shallow_think = use_shallow_think
+        self.use_shallow_talk = use_shallow_talk
+        self.use_complex_think_head = use_complex_think_head
+        self.use_complex_talk_head = use_complex_talk_head
+        self.use_weighted_talk_head = use_weighted_talk_head
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+class MistralConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
+    Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
+
+    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MistralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MistralModel, MistralConfig
+
+    >>> # Initializing a Mistral 7B style configuration
+    >>> configuration = MistralConfig()
+
+    >>> # Initializing a model from the Mistral 7B style configuration
+    >>> model = MistralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mistral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        attention_dropout=0.0,
+        max_thoughts=16,
+        merged_talk_heads=True,
+        merged_lm_and_talk_heads=False,
+        merged_lm_and_think_heads=True,
+        use_concat_talk_head=True,
+        use_shallow_think=True,
+        use_shallow_talk=False,
+        use_complex_think_head=False,
+        use_complex_talk_head=True,
+        use_weighted_talk_head=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.max_thoughts = max_thoughts
+        self.merged_talk_heads = merged_talk_heads
+        self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
+        self.merged_lm_and_think_heads = merged_lm_and_think_heads
+        self.use_concat_talk_head = use_concat_talk_head
+        self.use_shallow_think = use_shallow_think
+        self.use_shallow_talk = use_shallow_talk
+        self.use_complex_think_head = use_complex_think_head
+        self.use_complex_talk_head = use_complex_talk_head
+        self.use_weighted_talk_head = use_weighted_talk_head
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class MistralPreTrainedModel(PreTrainedModel):
+    config_class = MistralConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MistralDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class MistralModel(MistralPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
+
+    Args:
+        config: MistralConfig
+    """
+
+    def __init__(self, config: MistralConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            return_legacy_cache = True
+            logger.warning_once(
+                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
+                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
+            )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, use_cache, output_attentions
+        )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        use_cache: bool,
+        output_attentions: bool,
+    ):
+        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+
+        if self._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and use_cache:
+                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
+                if is_padding_right:
+                    raise ValueError(
+                        "You are attempting to perform batched generation with padding_side='right'"
+                        " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
+                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                    )
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+
+        # cache_position must be valid here no matter which cache we use
+        past_seen_tokens = cache_position[0] if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and not (using_static_cache or using_sliding_window_cache)
+            and not output_attentions
+        ):
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                sliding_window=self.config.sliding_window,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        # SlidingWindowCache
+        if using_sliding_window_cache:
+            target_length = max(sequence_length, self.config.sliding_window)
+        # StaticCache
+        elif using_static_cache:
+            target_length = past_key_values.get_max_length()
+        # DynamicCache or no cache
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
+            if attention_mask.max() != 0:
+                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            exclude_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            if self.config.sliding_window is not None:
+                if not using_sliding_window_cache or sequence_length > self.config.sliding_window:
+                    exclude_mask.bitwise_or_(
+                        torch.arange(target_length, device=device)
+                        <= (cache_position.reshape(-1, 1) - self.config.sliding_window)
+                    )
+            causal_mask *= exclude_mask
+            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                if attention_mask.dim() == 2:
+                    mask_length = attention_mask.shape[-1]
+                    padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                    padding_mask = padding_mask == 0
+                    causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                        padding_mask, min_dtype
+                    )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+
+############################## LM Heads   #################################
+
+
+
+
+
+
+################################ Tokenizer ##############################
+class MistralTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
+    no padding token in the original model.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+            attention mechanisms or loss computation.
+        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+
+        add_bos_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of sequences.
+        add_eos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of sequences.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not the default system prompt for Llama should be used.
+        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
+            Whether or not to add spaces between special tokens.
+        legacy (`bool`, *optional*):
+            Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
+            and #25224 which includes fixes to properly handle tokens that appear after special tokens.
+            Make sure to also set `from_slow` to `True`.
+            A simple example:
+
+            - `legacy=True`:
+            ```python
+            >>> from transformers import LlamaTokenizerFast
+
+            >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
+            >>> tokenizer.encode("Hello <s>.") # 869 is '▁.'
+            [1, 15043, 29871, 1, 869]
+            ```
+            - `legacy=False`:
+            ```python
+            >>> from transformers import LlamaTokenizerFast
+
+            >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
+            >>> tokenizer.encode("Hello <s>.")  # 29889 is '.'
+            [1, 15043, 29871, 1, 29889]
+            ```
+            Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
+        add_prefix_space (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+            other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        use_default_system_prompt=False,
+        spaces_between_special_tokens=False,
+        legacy=None,
+        add_prefix_space=True,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
+
+        if legacy is None:
+            logger.warning_once(
+                f"You are using the default legacy behaviour of the {self.__class__}. This is"
+                " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
+                " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
+                " means, and thoroughly read the reason why this was added as explained in"
+                " https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file"
+                " you can ignore this message"
+            )
+            legacy = True
+
+        self.legacy = legacy
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.use_default_system_prompt = use_default_system_prompt
+        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
+        self.add_prefix_space = add_prefix_space
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            use_default_system_prompt=use_default_system_prompt,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            legacy=legacy,
+            add_prefix_space=add_prefix_space,
+            **kwargs,
+        )
+
+    @property
+    def unk_token_length(self):
+        return len(self.sp_model.encode(str(self.unk_token)))
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
+    def get_spm_processor(self, from_slow=False):
+        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        if self.legacy or from_slow:  # no dependency on protobuf
+            tokenizer.Load(self.vocab_file)
+            return tokenizer
+
+        with open(self.vocab_file, "rb") as f:
+            sp_model = f.read()
+            model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
+            model = model_pb2.ModelProto.FromString(sp_model)
+            normalizer_spec = model_pb2.NormalizerSpec()
+            normalizer_spec.add_dummy_prefix = False
+            model.normalizer_spec.MergeFrom(normalizer_spec)
+            sp_model = model.SerializeToString()
+            tokenizer.LoadFromSerializedProto(sp_model)
+        return tokenizer
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
+    def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
+        """
+        Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
+        first token is special.
+        """
+        if self.legacy or len(text) == 0:
+            return super().tokenize(text, **kwargs)
+
+        text = text.replace(SPIECE_UNDERLINE, " ")
+        if self.add_prefix_space:
+            text = SPIECE_UNDERLINE + text
+
+        tokens = super().tokenize(text, **kwargs)
+
+        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
+            tokens = tokens[1:]
+        return tokens
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
+    def _tokenize(self, text, **kwargs):
+        """
+        Returns a tokenized string.
+
+        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
+        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
+        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
+        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
+        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
+        """
+        tokens = self.sp_model.encode(text, out_type=str)
+        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
+            return tokens
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        # since we manually add the prefix space, we have to remove it when decoding
+        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
+            tokens[0] = tokens[0][1:]
+
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for i, token in enumerate(tokens):
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special and i != 0 and self.legacy:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                if prev_is_special and i == 1 and self.add_prefix_space and not token.startswith(SPIECE_UNDERLINE):
+                    out_string += " "
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+        if token_ids_1 is not None:
+            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+        return output
+
+    @property
+    def default_chat_template(self):
+        """
+        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
+        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
+        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
+        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
+        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
+        to fine-tune a model with more flexible role ordering!
+
+        The output should look something like:
+
+        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
+        <bos>[INST] Prompt [/INST]
+
+        The reference for this chat template is [this code
+        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
+        in the original repository.
+        """
+        template = (
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
+            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = false %}"
+            "{% endif %}"
+            "{% for message in loop_messages %}"  # Loop over all non-system messages
+            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
+            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
+            "{% endif %}"
+            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
+            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
+            "{% else %}"
+            "{% set content = message['content'] %}"
+            "{% endif %}"
+            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
+            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
+            "{% elif message['role'] == 'system' %}"
+            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ ' '  + content.strip() + ' ' + eos_token }}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
+        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+
+        return template
+class MistralTokenizerFast(PreTrainedTokenizerFast):
+    """
+    Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    This uses notably ByteFallback and no normalization.
+
+    ```python
+    >>> from transformers import LlamaTokenizerFast
+
+    >>> tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
+    >>> tokenizer.encode("Hello this is a test")
+    [1, 15043, 445, 338, 263, 1243]
+    ```
+
+    If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
+    call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
+    values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
+    [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
+
+
+    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+    refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`, *optional*):
+            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        tokenizer_file (`str`, *optional*):
+            [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
+            contains everything needed to load the tokenizer.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        add_bos_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of sequences.
+        add_eos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of sequences.
+        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not the default system prompt for Llama should be used
+        legacy (`bool`, *optional*):
+            Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
+            and #25224 which includes fixes to properly handle tokens that appear after special tokens.
+            Make sure to also set `from_slow` to `True`.
+            A simple example:
+
+            - `legacy=True`:
+            ```python
+            >>> from transformers import LlamaTokenizerFast
+
+            >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
+            >>> tokenizer.encode("Hello <s>.") # 869 is '▁.'
+            [1, 15043, 29871, 1, 869]
+            ```
+            - `legacy=False`:
+            ```python
+            >>> from transformers import LlamaTokenizerFast
+
+            >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
+            >>> tokenizer.encode("Hello <s>.")  # 29889 is '.'
+            [1, 15043, 29871, 1, 29889]
+            ```
+            Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
+        add_prefix_space (`bool`, *optional*):
+            Whether or not the tokenizer should automatically add a prefix space
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    slow_tokenizer_class = MistralTokenizer
+    padding_side = "left"
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file=None,
+        tokenizer_file=None,
+        clean_up_tokenization_spaces=False,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        add_bos_token=True,
+        add_eos_token=False,
+        use_default_system_prompt=False,
+        legacy=None,
+        add_prefix_space=None,
+        **kwargs,
+    ):
+        if legacy is None:
+            logger.warning_once(
+                f"You are using the default legacy behaviour of the {self.__class__}. This is"
+                " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
+                " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
+                " means, and thoroughly read the reason why this was added as explained in"
+                " https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file"
+                " you can ignore this message."
+            )
+            legacy = True
+        self.legacy = legacy
+
+        if add_prefix_space is not None:
+            kwargs["from_slow"] = True
+
+        super().__init__(
+            vocab_file=vocab_file,
+            tokenizer_file=tokenizer_file,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            use_default_system_prompt=use_default_system_prompt,
+            add_prefix_space=add_prefix_space,
+            legacy=legacy,
+            **kwargs,
+        )
+        self._add_bos_token = add_bos_token
+        self._add_eos_token = add_eos_token
+        self.update_post_processor()
+        self.use_default_system_prompt = use_default_system_prompt
+        self.vocab_file = vocab_file
+
+    @property
+    def can_save_slow_tokenizer(self) -> bool:
+        return os.path.isfile(self.vocab_file) if self.vocab_file else False
+
+    def update_post_processor(self):
+        """
+        Updates the underlying post processor with the current `bos_token` and `eos_token`.
+        """
+        bos = self.bos_token
+        bos_token_id = self.bos_token_id
+        if bos is None and self.add_bos_token:
+            raise ValueError("add_bos_token = True but bos_token = None")
+
+        eos = self.eos_token
+        eos_token_id = self.eos_token_id
+        if eos is None and self.add_eos_token:
+            raise ValueError("add_eos_token = True but eos_token = None")
+
+        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
+        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
+
+        special_tokens = []
+        if self.add_bos_token:
+            special_tokens.append((bos, bos_token_id))
+        if self.add_eos_token:
+            special_tokens.append((eos, eos_token_id))
+        self._tokenizer.post_processor = processors.TemplateProcessing(
+            single=single, pair=pair, special_tokens=special_tokens
+        )
+
+    @property
+    def add_eos_token(self):
+        return self._add_eos_token
+
+    @property
+    def add_bos_token(self):
+        return self._add_bos_token
+
+    @add_eos_token.setter
+    def add_eos_token(self, value):
+        self._add_eos_token = value
+        self.update_post_processor()
+
+    @add_bos_token.setter
+    def add_bos_token(self, value):
+        self._add_bos_token = value
+        self.update_post_processor()
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not self.can_save_slow_tokenizer:
+            raise ValueError(
+                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+                "tokenizer."
+            )
+
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file,)
+
+    @property
+    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.default_chat_template
+    def default_chat_template(self):
+        """
+        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
+        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
+        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
+        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
+        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
+        to fine-tune a model with more flexible role ordering!
+
+        The output should look something like:
+
+        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
+        <bos>[INST] Prompt [/INST]
+
+        The reference for this chat template is [this code
+        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
+        in the original repository.
+        """
+        template = (
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
+            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = false %}"
+            "{% endif %}"
+            "{% for message in loop_messages %}"  # Loop over all non-system messages
+            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
+            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
+            "{% endif %}"
+            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
+            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
+            "{% else %}"
+            "{% set content = message['content'] %}"
+            "{% endif %}"
+            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
+            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
+            "{% elif message['role'] == 'system' %}"
+            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ ' '  + content.strip() + ' ' + eos_token }}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
+        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+
+        return template
+
+    # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
+    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+################################ Tokenizer ##############################
+
+
+
+
+################################  UNIVERSAL NN COMPONENTS ################################
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
+
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class MistralRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MistralRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+class MistralRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+################################  UNIVERSAL Functions ################################
+def nonzero_mean(x, axis=None):
+    if axis is not None:
+        return x.sum(axis) / (x != 0).sum(axis)
+    return x.sum() / (x != 0).sum()
+def loss_mean(x):
+    return x.sum() / (x != 0).sum()
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos[:,:, -q.shape[2]:]) + (rotate_half(q) * sin[:,:, -q.shape[2]:]) if q is not None else None
+    k_embed = (k * cos) + (rotate_half(k) * sin) if k is not None else None
+    return q_embed, k_embed
+
+def apply_grouped_rotary_pos_emb(q, k, cos, sin, position_ids, g_size_1=1, g_size_2=4096):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    position_ids_q = position_ids//g_size_1 + g_size_2 - g_size_2//g_size_1
+    position_ids_k = position_ids//g_size_1
+
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos_q = cos[position_ids_q].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin_q = sin[position_ids_q].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    cos_k = cos[position_ids_k].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin_k = sin[position_ids_k].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos_q) + (rotate_half(q) * sin_q) if q is not None else None
+    k_embed = (k * cos_k) + (rotate_half(k) * sin_k) if k is not None else None
+
+    return q_embed, k_embed
+
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
+) -> float:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        attention_mask (`torch.Tensor`, None):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+        num_experts (`int`, *optional*):
+            Number of experts
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .reshape(-1, top_k, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+            .reshape(-1, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+    return overall_loss * num_experts
+
+class MistralMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state):
+        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+class MistralAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+
+        self.rotary_emb = MistralRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+class MistralFlashAttention2(MistralAttention):
+    """
+    Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+        if isinstance(past_key_value, StaticCache):
+            raise ValueError(
+                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+            )
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += cache_position[0]
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            sliding_window=getattr(self.config, "sliding_window", None),
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral
+class MistralSdpaAttention(MistralAttention):
+    """
+    Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MistralAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+
+            logger.warning_once(
+                "MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and causal_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = True if causal_mask is None and q_len > 1 else False
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+MISTRAL_ATTENTION_CLASSES = {
+    "eager": MistralAttention,
+    "flash_attention_2": MistralFlashAttention2,
+    "sdpa": MistralSdpaAttention,
+}
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->Mistral, LLAMA->MISTRAL
+class MistralDecoderLayer(nn.Module):
+    def __init__(self, config: MistralConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = MistralMLP(config)
+        self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+class MixtralBlockSparseTop2MLP(nn.Module):
+    def __init__(self, config: MixtralConfig):
+        super().__init__()
+        self.ffn_dim = config.intermediate_size
+        self.hidden_dim = config.hidden_size
+
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+class MixtralSparseMoeBlock(nn.Module):
+    """
+    This implementation is
+    strictly equivalent to standard MoE with full capacity (no
+    dropped tokens). It's faster since it formulates MoE operations
+    in terms of block-sparse operations to accomodate imbalanced
+    assignments of tokens to experts, whereas standard MoE either
+    (1) drop tokens at the cost of reduced performance or (2) set
+    capacity factor to number of experts and thus waste computation
+    and memory on padding.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_dim = config.hidden_size
+        self.ffn_dim = config.intermediate_size
+        self.num_experts = config.num_local_experts
+        self.top_k = config.num_experts_per_tok
+
+        # gating
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+        self.experts = nn.ModuleList([MixtralBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+        # Jitter parameters
+        self.jitter_noise = config.router_jitter_noise
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """ """
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        if self.training and self.jitter_noise > 0:
+            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # One hot encode the selected experts to create an expert mask
+        # this will be used to easily index which expert is going to be sollicitated
+        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            idx, top_x = torch.where(expert_mask[expert_idx])
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+class MixtralDecoderLayer(nn.Module):
+    def __init__(self, config: MixtralConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+        self.mlp = MistralMLP(config)
+        self.block_sparse_moe = MixtralSparseMoeBlock(config)
+        self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+                should not be returned during inference.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states, router_logits = self.block_sparse_moe(hidden_states)
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+################################  closed COMPONENTS ################################
+
+
+############# Causal LM ################# 
+class MistralForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MistralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.max_thoughts = config.max_thoughts
+        self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads
+        self.use_concat_talk_head = config.use_concat_talk_head
+        self.use_shallow_talk = config.use_shallow_talk
+        self.use_complex_talk_head = config.use_complex_talk_head
+        self.use_weighted_talk_head = config.use_weighted_talk_head
+        # the weighted head will output a single value, so it can't be passed to the lm head
+        assert not (self.use_weighted_talk_head and self.use_shallow_talk)
+
+        self.n_ahead = 1
+        self.n_ahead_talk = 1
+        self.n_passes = 1
+        self.n_tokens_print = 1
+        self.gradient_accumulation_steps = 1
+        self.training_steps = 0
+        self.tokenizer = None
+        self.start_token_id = None
+        self.end_token_id = None
+        self.rm_initialized = False
+        self.residual_talk_head = True
+        self.thought_init_std_scale = 1e-2
+
+        self.final_only_mode = False
+        self.first_and_last_mode = True
+        self.first_only = False
+        self.original_loss_weight = 0.5
+
+        self.cumulative_residual = False
+        self.clever_residual = False
+        self.skip_residual = False
+        self.no_residual = True
+
+        self.optimize_lm_head_only_at_start = False
+        self.optimize_model_only_at_start = False
+
+        if self.optimize_model_only_at_start:
+            raise NotImplementedError
+        self.train_only_thinking_embedding = False
+        self.weighted_embeddings = False
+        self.use_start_thought_token = True
+        self.use_end_thought_token = True
+        self.initialize_thought_embedding_to_normal = False
+        self.initial_start_token = "---"
+        self.initial_end_token = "---"
+        self.output_logits_at_the_end = True
+
+        self.gumbel_temperature = 0.001
+
+        self.use_policy_loss = True
+        self.include_policy_loss = True
+        self.trice_mode = True
+        self.remove_negative_rewards = True
+        self.use_policy_loss_for_end_thought = True
+        
+        self.base_original_mode = False
+        self.original_mode = False
+
+        self.thought_prefix = "(Let's think step by step"
+        self.tokenized_thought_prefix = None
+        self.log_dict = defaultdict(int)
+        self.eval_log_dict = defaultdict(int)
+        self.print_final_only = True
+        self.loss_mean = loss_mean
+        self.all_rewards = []
+        self.all_unreduced_losses = []
+        self.kill_after = 100
+
+        self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+        self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+
+        self.policy_loss_beta = 1e6
+        self.embedding_scale = 1e2
+        self.reinforce_temperature = 3
+        self.base_loss_beta = 1
+
+        # Not used in the paper:
+        self.use_thought_prefix = False
+        self.use_reparam_for_thought_embeddings = False
+        self.use_upper_triangular = False
+        self.subtract_mean_reward = False
+        self.comparison_mode = False
+        self.gumbel_detach = True
+    
+        # For visualization
+        self.eval_mode = False
+
+        num_talk = 1
+        talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
+        if self.use_weighted_talk_head:
+            talk_output_dim = 1
+        else:
+            talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size
+
+        if not self.merged_lm_and_talk_heads:
+            if self.use_complex_talk_head:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, talk_output_dim, bias=False)
+                )])
+            else:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, talk_output_dim, bias=False)
+                )])
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+    def calculate_policy_loss(self, thoughts, rewards):
+        thought_log_probs = []
+        for thought in thoughts:
+            thought_log_prob = self.lm_head(thought).log_softmax(dim=-1)
+            thought_log_probs.append(thought_log_prob)
+        
+        thought_log_probs = torch.stack(thought_log_probs, dim=1)  # (batch_size, num_thoughts, seq_length, vocab_size)
+        thought_probs = torch.exp(thought_log_probs)
+        
+        policy_loss = -torch.mean(thought_log_probs * rewards.unsqueeze(-1).unsqueeze(-1))
+        
+        return policy_loss
+    
+    def _generate_thoughts(self, hidden_states, max_length):
+        batch_size = hidden_states.size(0)
+        thought_ids = torch.zeros((batch_size, self.config.max_thoughts, max_length), dtype=torch.long, device=hidden_states.device)
+        thought_embeddings = []
+
+        for i in range(self.config.max_thoughts):
+            thought_input_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=hidden_states.device)
+            thought_outputs = self.generate(
+                input_ids=thought_input_ids,
+                max_length=max_length,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                pad_token_id=self.config.pad_token_id,
+                eos_token_id=self.config.eos_token_id,
+            )
+            thought_ids[:, i, :] = thought_outputs
+            thought_embeddings.append(self.get_input_embeddings()(thought_outputs))
+
+        thought_embeddings = torch.stack(thought_embeddings, dim=1)
+        return thought_ids, thought_embeddings
+
+    @torch.no_grad()
+    def infer(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        batch_size, seq_len = input_ids.shape
+
+        # Save the original input_ids and attention_mask for later use
+        original_input_ids = input_ids.clone()
+        original_attention_mask = attention_mask.clone() if attention_mask is not None else None
+
+        # Append the start thought token to the input sequence
+        start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Generate the continuation
+        continuation_length = self.n_ahead - 2
+        new_key_values = past_key_values
+        
+        start_time = time.time()
+        for continuation_idx in range(continuation_length):
+            outputs = self.model(
+                input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device),
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=new_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            new_key_values = outputs.past_key_values
+
+            hidden_states = outputs[0]
+
+            logits = self.lm_head(hidden_states)
+            logits = logits[:, -1, :]  # Only consider the last token
+
+            # Apply Gumbel-Softmax to the logits
+            next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1)
+            next_token_id = torch.argmax(next_token_logits, dim=-1)
+
+            # Append the generated token to the input sequence
+            input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1)
+            seq_len += 1
+
+            # Update the attention mask
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Append the end thought token to the input sequence
+        end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Get the hidden states before and after the thought
+        outputs_before = self.model(
+            input_ids=original_input_ids,
+            attention_mask=original_attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_before = outputs_before[0][:, -1:, :]
+
+        # two new tokens: last continuation token and end thought token
+        outputs_after = self.model(
+            input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=new_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_after = outputs_after[0][:, -1:, :]
+
+        # Apply the talk head to get the mixing weight
+        mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1))
+
+        # Apply the mixing weight to the hidden states
+        mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after
+
+        # Apply the language model head to get the final logits
+        logits = self.lm_head(mixed_hidden_states)
+        return logits
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        log_dict = self.log_dict if self.training else self.eval_log_dict
+
+        if self.training and self.kill_after is not None and self.training_steps // self.gradient_accumulation_steps > self.kill_after:
+            raise ValueError("Killed after")
+
+        if not self.training:
+            n_ahead_talk_to_restore = self.n_ahead_talk
+            n_passes_to_restore = self.n_passes
+            self.n_ahead_talk = 1
+            self.n_passes = 1
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual
+        assert not (self.skip_residual and self.use_policy_loss)
+
+        if self.tokenized_thought_prefix is None and self.use_thought_prefix:
+            self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"]
+
+        def apply_head(head, states, detach=False):
+            if detach:
+                head_weight = head.weight.detach()
+            else:
+                head_weight = head.weight
+            head_weight = head_weight.to(states.device)
+            return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous()
+    
+        def idx_if_sequential(head, idx=0):
+            if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList):
+                return idx_if_sequential(head[idx], idx=idx)
+            return head
+
+        def none_repeat_interleave(x, n):
+            if x is None:
+                return x
+            return x.repeat_interleave(n, dim=0)
+
+        if self.n_passes > 1:
+            input_ids = none_repeat_interleave(input_ids, self.n_passes)
+            attention_mask = none_repeat_interleave(attention_mask, self.n_passes)
+            position_ids = none_repeat_interleave(position_ids, self.n_passes)
+            inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes)
+            labels = none_repeat_interleave(labels, self.n_passes)
+            if past_key_values is not None:
+                past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values]
+        cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device)
+
+        self.tokenizer_has_start_thought_token = True
+        self.tokenizer_has_end_thought_token = True
+        if self.start_token_id is None:
+            self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+            if self.start_token_id == 0:
+                self.start_token_id = self.tokenizer.bos_token_id
+                self.tokenizer_has_start_thought_token = False
+            elif self.use_start_thought_token:
+                # base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token)
+                base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0]
+                if self.initialize_thought_embedding_to_normal:
+                    self.start_embedding.data = torch.zeros_like(self.start_embedding.data)
+                else:
+                    self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale
+                self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
+        if self.end_token_id is None:
+            self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+            if self.end_token_id == 0:
+                self.end_token_id = self.tokenizer.eos_token_id
+                self.tokenizer_has_end_thought_token = False
+            elif self.use_end_thought_token:
+                # base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token)
+                base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0]
+                if self.initialize_thought_embedding_to_normal:
+                    self.end_embedding.data = torch.zeros_like(self.end_embedding.data)
+                else:
+                    self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale
+                self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
+
+        if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode):
+            self.rm_initialized = True                        
+            if not self.use_shallow_talk:
+                head = self.talk_head[0]
+                cur_head = head[-1] if isinstance(head, nn.Sequential) else head
+                talk_input_dim = cur_head.weight.data.shape[1]
+                talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0]
+                cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype)
+            else:
+                # convert to identity transform
+                def lambda_transform(cur_head):
+                    if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]:
+                        return torch.cat([
+                        torch.eye(
+                            cur_head.weight.data.shape[0],
+                            device=cur_head.weight.device,
+                            dtype=cur_head.weight.dtype
+                        ),
+                        torch.zeros(
+                            cur_head.weight.data.shape[0],
+                            cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0],
+                            device=cur_head.weight.device,
+                            dtype=cur_head.weight.dtype
+                        )], dim=1)
+                    return torch.eye(
+                        cur_head.weight.data.shape[0],
+                        device=cur_head.weight.device,
+                        dtype=cur_head.weight.dtype
+                    )
+                if isinstance(self.talk_head[0], nn.Sequential):
+                    for cur_head in self.talk_head[0]:
+                        # if it has weights
+                        if hasattr(cur_head, "weight"):
+                            cur_head.weight.data = lambda_transform(cur_head)
+                else:
+                    self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0])
+
+        loss = None
+        prev_rm_tokens = None
+        cur_rm_tokens = None
+        prev_rm_logits = None
+        prev_sample_probs = None
+        did_skip_sampling = None
+        skip_sampling = None
+        sample_probs = None
+        hidden_states = None
+        logits = None
+        talk_kl_penalty = None
+        rm_logits = None
+        residual_logits = None
+        probabilities_2d = None
+        prev_probabilities_2d = None
+        policy_reward = None
+        logits_to_output = None
+        batch_size, seq_len = input_ids.shape
+        base_input_ids = input_ids.clone()
+        loss_list = []
+        dqn_loss_list = []
+        sampled_token_history = []
+        sample_probs_history = []
+        action_loglikelihoods_list = []
+
+        if self.use_end_thought_token or self.use_start_thought_token:
+            if not self.use_reparam_for_thought_embeddings:
+                start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale
+                end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale
+            else:
+                start_embedding = self.start_embedding * self.embedding_scale
+                end_embedding = self.end_embedding * self.embedding_scale
+            base_embeddings = self.model.embed_tokens.weight
+            if self.train_only_thinking_embedding:
+                base_embeddings = base_embeddings.detach()
+        # # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1
+        for ahead_idx in range(fwd_iters):
+            past_key_values_length = 0
+            if past_key_values is not None:
+                use_legacy_cache = not isinstance(past_key_values, Cache)
+                if use_legacy_cache:
+                    past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                past_key_values_length = past_key_values.get_usable_length(seq_len)
+
+            if position_ids is None:
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+                position_ids = torch.arange(
+                    past_key_values_length, seq_len + past_key_values_length, dtype=torch.long, device=device
+                )
+                position_ids = position_ids.unsqueeze(0).view(-1, seq_len)
+            else:
+                position_ids = position_ids.view(-1, seq_len).long()
+
+            if inputs_embeds is None:
+                contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any()
+                contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any()
+                contains_thought = contains_start or contains_end
+                if contains_thought:
+                    thought_id = self.start_token_id if contains_start else self.end_token_id
+                    cur_thought_embedding = start_embedding if contains_start else end_embedding
+                    if self.use_reparam_for_thought_embeddings:
+                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
+                        inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
+                        if contains_start:
+                            sampled_start = inputs_embeds.clone().detach()
+                        if contains_end:
+                            sampled_end = inputs_embeds.clone().detach()
+                    else:
+                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
+                else:
+                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
+                        inputs_embeds = self.model.embed_tokens(input_ids)
+            
+            if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode:
+                if attention_mask is None:
+                    base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device)
+                    base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len)
+                    base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1)
+                    attention_mask = base_attention_mask
+                    breakpoint()
+                elif attention_mask.dim() == 2:
+                    if seq_len + past_key_values_length != attention_mask.shape[-1]:
+                        breakpoint()
+                        attention_mask = torch.cat(
+                            [torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask],
+                            dim=-1
+                        )
+                    # # if the attention mask 
+                    attention_mask = _prepare_4d_causal_attention_mask(
+                        attention_mask,
+                        (batch_size, seq_len),
+                        inputs_embeds,
+                        past_key_values_length,
+                        sliding_window=self.config.sliding_window,
+                    )
+
+            outputs = self.model(
+                # input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            prev_hidden_states = hidden_states
+            hidden_states = outputs[0]
+            prev_rm_logits = rm_logits  # for policy gradient
+            prev_rm_tokens = cur_rm_tokens  # for policy gradient
+
+            if ahead_idx == 0:
+                hidden_states_lm = hidden_states
+                logits = self.lm_head(hidden_states_lm)
+                base_hidden_states = hidden_states.clone()
+                initial_loss_logits = logits.clone()
+                if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start:
+                    logits = logits.detach()
+                    base_hidden_states = base_hidden_states.detach()
+                if self.optimize_model_only_at_start:
+                    hidden_states = hidden_states.detach()
+                base_logits = logits.clone()
+            else:
+                talk_hidden_states = hidden_states
+                if self.merged_lm_and_talk_heads:
+                    assert self.no_residual
+                    residual_logits = self.lm_head(hidden_states)
+                    talk_hidden_states = hidden_states
+                else:
+                    if ahead_idx > self.n_ahead - 1:
+                        cur_base_hidden = torch.cat([
+                            base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                    else:
+                        cur_base_hidden = base_hidden_states
+
+                    if self.use_concat_talk_head:
+                        # concatenate the hidden states with the original hidden states
+                        head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1)
+                    else:
+                        head_input_hidden_states = talk_hidden_states
+
+                    residual_logits = self.talk_head[0](head_input_hidden_states)
+                    if self.use_shallow_talk:
+                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)                        
+                    residual_logits = residual_logits.to(logits.device)
+                    if self.use_weighted_talk_head:
+                        # combine the cur_base_hidden with the talk_hidden_states according to the weighted head
+                        residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits
+                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)
+
+                assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1
+                if self.clever_residual:
+                    if ahead_idx >= self.n_ahead - 1:
+                        # get the logits shifted according to the current talk ahead
+                        cur_base_logits = torch.cat([
+                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                        if self.optimize_lm_head_only_at_start:
+                            cur_base_logits = cur_base_logits.detach()
+                        logits = cur_base_logits + residual_logits
+                    else:
+                        logits += residual_logits / self.n_ahead
+                elif self.cumulative_residual:
+                    if self.residual_talk_head:
+                        if ahead_idx < self.n_ahead:
+                            logits += residual_logits
+                        else:
+                            # get the logits shifted according to the current talk ahead
+                            cur_base_logits = torch.cat([
+                                base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                                base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                            ], dim=-2)
+                            if self.optimize_lm_head_only_at_start:
+                                cur_base_logits = cur_base_logits.detach()
+                            logits = cur_base_logits + residual_logits
+                    else:
+                        if ahead_idx < self.n_ahead:
+                            logits += residual_logits
+                        else:
+                            logits = residual_logits
+                elif self.skip_residual:
+                    if ahead_idx >= self.n_ahead:
+                        # get the logits shifted according to the current talk ahead
+                        cur_base_logits = torch.cat([
+                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                        if self.optimize_lm_head_only_at_start:
+                            cur_base_logits = cur_base_logits.detach()
+                        logits = cur_base_logits
+                elif self.no_residual:
+                    logits = residual_logits
+                else:
+                    logits = base_logits + residual_logits
+
+            attempted = False
+            talk_loss_list = []
+            if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0):
+                loss = None
+                attempted = True
+
+                if labels is not None:
+                    for shift_amount in range(self.n_ahead_talk):
+                        # Shift so that tokens < n predict n
+                        #  ab[cde]f
+                        # abc[def]
+                        if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
+                            loss_logits = initial_loss_logits
+                        else:
+                            loss_logits = logits
+                        shift_logits = loss_logits[..., shift_amount:-1, :].contiguous()
+                        shift_labels = labels[..., 1 + shift_amount:].contiguous()
+                        # Flatten the tokens
+                        loss_fct = CrossEntropyLoss(reduction="none")
+                        shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                        shift_labels = shift_labels.view(-1).clone()
+                        # Enable model parallelism
+                        shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100
+                        shift_labels = shift_labels.to(shift_logits.device)
+                        loss = loss_fct(shift_logits, shift_labels)
+                        if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode:
+                            loss_list.append(loss)
+                        talk_loss_list.append(nonzero_mean(loss).detach())
+            
+            if not attempted or self.comparison_mode:
+                rm_hidden_states = hidden_states
+                # print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm())
+                rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start)
+                    
+                # don't allow it to predict the thinking token
+                if self.tokenizer_has_start_thought_token:                    
+                    rm_logits[..., self.start_token_id] = -1e10
+                if self.tokenizer_has_end_thought_token:
+                    rm_logits[..., self.end_token_id] = -1e10
+                probabilities = rm_logits
+                if probabilities_2d is not None:
+                    prev_probabilities_2d = probabilities_2d.clone()
+                probabilities_2d = probabilities.view(-1, probabilities.size(-1))
+
+                did_skip_sampling = skip_sampling
+                skip_sampling = False
+                if ahead_idx == 0 and self.use_start_thought_token:
+                    override_token = self.start_token_id
+                elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]:
+                    override_token = self.tokenized_thought_prefix[..., ahead_idx]
+                elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token:
+                    override_token = self.end_token_id
+                else:
+                    override_token = None
+                if override_token is not None and self.n_ahead > 1:
+                    # always start with the start token
+                    probabilities_2d = torch.zeros_like(probabilities_2d)
+                    probabilities_2d[:, override_token] = 1.0
+                    skip_sampling = True
+                elif ahead_idx >= self.n_ahead - 1:
+                    if labels is not None:  # we're in the talk phase
+                        cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1
+                        # print("Setting rm to labels", cur_talk_n, "during", ahead_idx)
+                        shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device)
+                        padding = torch.full_like(
+                            labels[..., :cur_talk_n],
+                            self.tokenizer.pad_token_id,
+                            dtype=torch.long,
+                            device=shift_labels.device
+                        )
+                        new_rm_tokens = torch.cat(
+                            [shift_labels, padding],
+                            dim=-1
+                        )
+                        # convert rm tokens to one-hot
+                        probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype)
+                        skip_sampling = True
+                    else:
+                        continue
+                temperature = self.gumbel_temperature if self.training else 0.001
+                prev_sample_probs = sample_probs
+                sample_probs = probabilities_2d
+                if ahead_idx < self.n_ahead - 1 and not skip_sampling:
+                    probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1)
+                    if self.gumbel_detach:
+                        probabilities_2d = probabilities_2d.detach()
+                sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu())
+                # convert rm logits directly to embeddings
+                contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0)
+                contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0)
+                contains_thought = contains_start or contains_end
+
+                if not contains_thought:
+                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
+                        inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype))
+                else:
+                    thought_id = self.start_token_id if contains_start else self.end_token_id
+                    cur_thought_embedding = start_embedding if contains_start else end_embedding
+                    if self.use_reparam_for_thought_embeddings:
+                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
+                        inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
+                        if contains_start:
+                            sampled_start = inputs_embeds.clone().detach()
+                        else:
+                            sampled_end = inputs_embeds.clone().detach()
+                    else:
+                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
+                        inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
+                inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
+
+                if len(attention_mask.shape) == 2:
+                    breakpoint()
+                else:
+                    original_attention = attention_mask[..., :attention_mask.shape[-2]]
+                    if self.use_upper_triangular:
+                        new_attention = original_attention
+                    else:
+                        original_attention = original_attention == attention_mask.max()
+                        # because eye isn't implemented for BF16, we need to handle the case
+                        if not attention_mask.dtype == torch.bfloat16:
+                            new_attention = torch.eye(
+                                seq_len, dtype=attention_mask.dtype, device=attention_mask.device
+                            )
+                        else:
+                            new_attention = torch.eye(
+                                seq_len, dtype=torch.float32, device=attention_mask.device
+                            ).to(attention_mask.dtype)
+
+                        new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1)
+                        new_attention = new_attention * original_attention
+                        new_attention[new_attention == 0] = attention_mask.min()
+                        new_attention[new_attention == 1] = attention_mask.max()
+                    attention_mask = torch.cat([attention_mask, new_attention], dim=-1)
+                past_key_values = outputs.past_key_values
+                position_ids = position_ids + 1
+
+                if labels is not None and (self.n_ahead > 1 or not self.base_original_mode):
+                    # Shift so that tokens < n predict n
+                    # logits: abcdef -> bcdef? -> cdef??
+                    # labels: abcdef -> ?bcdef -> ??cdef
+                    if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
+                        loss_logits = initial_loss_logits
+                    else:
+                        loss_logits = logits
+                    shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1))
+                    shift_logits = loss_logits[..., :-shift_idx, :].contiguous()
+                    shift_labels = labels[..., shift_idx:].contiguous()
+                    # Flatten the tokens
+                    loss_fct = CrossEntropyLoss(reduction="none")
+                    shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                    shift_labels = shift_labels.view(-1)
+                    # Enable model parallelism
+                    shift_labels = shift_labels.to(shift_logits.device)
+                    # if shift_labels.min() == self.tokenizer.pad_token_id:
+                    shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels)
+                    unreduced_loss = loss_fct(shift_logits, shift_labels)
+                    if torch.any(unreduced_loss != unreduced_loss):
+                        raise ValueError("NaN loss")
+                    unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1)
+                    loss_list.append(unreduced_loss)
+
+
+                    if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token):
+                        # we treat the change in loss as the reward
+                        previous_loss = loss_list[-2]
+                        # for example, suppose n_ahead = 3 and n_ahead_talk = 2
+                        # note that we end at self.n_ahead + self.n_ahead_talk - 2
+                        # in this case, 5 - 2 = 3, so we end at ahead_idx = 3
+                        # we also predict the next token at ahead_idx = 2
+                        # when we get to ahead_idx = 2, we predict ahead
+                        # so we shift by 1
+                        # note that this is ahead_idx = n_ahead - 1
+                        # when we get to ahead_idx = 3, we predict ahead
+                        # so we shift by 2
+                        # note that this is ahead_idx = n_ahead
+                        if ahead_idx < self.n_ahead - 1:
+                            shift_amount = 0
+                            original_dqn_reward = (previous_loss - unreduced_loss).detach()
+                            if self.first_and_last_mode:
+                                original_dqn_reward = original_dqn_reward * 0.0
+                        else:
+                            # logits vs cur_policy_shift_logits
+                            # let's look at rm_logits and prev_rm_logits
+                            shift_amount = max(0, ahead_idx - (self.n_ahead - 1))
+                            # let's say shift_amount = 2
+                            # abcdefg -> bcdefg? -> cdefg??
+                            # logits = [a b]c d e f[g]
+                            # labels = [a b c]d e f g
+                            cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach()
+                            cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous()
+                            # Flatten the tokens
+                            cur_policy_loss_fct = CrossEntropyLoss(reduction="none")
+                            cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size)
+                            cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone()
+                            # Enable model parallelism
+                            cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100
+                            cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device)
+                            cur_policy_reward_base_loss = loss_fct(
+                                cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device)
+                            ).reshape(logits.shape[0], -1)
+                            original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss
+                                
+                        if not did_skip_sampling:
+                            nonzero_indices = prev_probabilities_2d.nonzero()
+                            action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]]
+                            action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount]
+                            action_loglikelihoods_list.append(action_loglikelihoods_2d)
+                        if policy_reward is None:
+                            policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
+                        else:
+                            if self.n_ahead_talk > shift_amount:
+                                added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
+                            else:
+                                added_reward = original_dqn_reward
+                            policy_reward += added_reward
+                    
+                    if self.use_policy_loss and ahead_idx == self.n_ahead + self.n_ahead_talk - 2:
+                        # only compute during the thinking phase
+                        if self.use_reparam_for_thought_embeddings and (self.use_start_thought_token or self.use_end_thought_token):
+                            # sampled_start, sampled_end
+                            # calculate the log likelihood of the start and end embeddings sampled from a multivariate normal distribution
+                            # with mean start_embedding[0] and standard deviation start_embedding[1]
+                            if self.use_start_thought_token:
+                                exp_start_std = torch.exp(start_embedding[1])
+                                start_loglikelihood = -0.5 * (sampled_start.detach() - start_embedding[0]) ** 2 / exp_start_std ** 2 - start_embedding[1] - 0.5 * math.log(2 * math.pi)
+                                start_loglikelihood = start_loglikelihood.mean(dim=-1)
+                            if self.use_end_thought_token:
+                                exp_end_std = torch.exp(end_embedding[1])
+                                end_loglikelihood = -0.5 * (sampled_end.detach() - end_embedding[0]) ** 2 / exp_end_std ** 2 - end_embedding[1] - 0.5 * math.log(2 * math.pi)
+                                end_loglikelihood = end_loglikelihood.mean(dim=-1)
+                            # we use the mean instead of the sum to prevent dependence on the dimensionality of the embeddings
+                            if self.use_end_thought_token and self.use_policy_loss_for_end_thought:
+                                action_loglikelihoods_list.append(end_loglikelihood)
+                            if self.use_start_thought_token:
+                                action_loglikelihoods_list.append(start_loglikelihood)                                
+
+                        if ahead_idx == self.n_ahead + self.n_ahead_talk - 2 and self.eval_mode:
+                            with torch.no_grad():
+                                # calculate the 0.75 quantile of the rewards
+                                filtered_tokens = input_ids[:, :policy_reward.shape[-1]].cpu().detach().numpy().flatten()
+                                filtered_tokens_mask = filtered_tokens != self.tokenizer.pad_token_id
+                                filtered_tokens = filtered_tokens[filtered_tokens_mask]
+                                filtered_rewards = policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten()
+                                filtered_rewards = filtered_rewards[filtered_tokens_mask]
+
+                                abs_reward_list = np.abs(policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten())
+                                abs_reward_list = abs_reward_list[filtered_tokens_mask]
+                                medium_quantile = np.quantile(abs_reward_list, 0.5)
+                                upper_quantile = np.quantile(abs_reward_list, 0.95)
+
+                                save_tokens_with_rewards_to_pdf(
+                                    filtered_tokens,
+                                    [0] + filtered_rewards.tolist(),
+                                    self.tokenizer,
+                                    output_file=f"texts/rewards_talk_{self.n_ahead_talk}_{self.training_steps}.pdf",
+                                    eps=medium_quantile,
+                                    eps2=upper_quantile,
+                                )
+
+                                def plot_kde(data, losses):
+                                    sns.set(style="whitegrid")
+                                    # Create the KDE plot
+                                    sns.kdeplot(data, fill=True)
+                                    # Set the plot title and labels
+                                    plt.title("KDE Plot")
+                                    plt.xlabel("Value")
+                                    plt.ylabel("Density")
+                                    # Save the plot
+                                    plt.savefig(f"texts/kde_talk_{self.n_ahead_talk}_{self.training_steps}.pdf")
+                                    # Close the plot
+                                    plt.close()
+
+                                    # Step 1: Create a base color palette
+                                    base_colors = sns.color_palette("light:#5A9", n_colors=256)  # More colors for a smoother gradient
+                                    base_cmap = LinearSegmentedColormap.from_list("log_light", base_colors)
+                                    log_norm = LogNorm(vmin=1e-3, vmax=10)
+
+                                    sns.kdeplot(x=data, y=losses, fill=True, levels=20, norm=log_norm, cut=0, linewidths=0)
+                                    # limit y to 0 to 25 and x to -1 to 1
+                                    plt.xlim(-1, 1)
+                                    plt.ylim(0, 25)
+                                    plt.savefig(f"texts/jointer_talk_{self.n_ahead_talk}_{self.training_steps}.pdf")
+                                    plt.close()
+
+                                self.all_rewards.extend(filtered_rewards)
+                                self.all_unreduced_losses.extend(unreduced_loss[:, :-1].flatten()[filtered_tokens_mask].float().flatten().cpu().detach().numpy())
+                                plot_kde(self.all_rewards, self.all_unreduced_losses)
+
+                        for action_loglikelihoods_2d in action_loglikelihoods_list:
+                            train_policy_reward = policy_reward
+
+                            # discard rewards below the mean
+                            if self.trice_mode and self.n_passes > 1:
+                                batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1])
+                                # average over the passes
+                                train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True)
+                                train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1])
+                                
+                            if self.subtract_mean_reward:
+                                train_policy_reward = train_policy_reward - train_policy_reward.mean()
+                            if self.remove_negative_rewards:
+                                fixed_policy_reward = train_policy_reward.detach().clamp(min=0)
+                            else:
+                                fixed_policy_reward = train_policy_reward.detach()
+                            actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device)
+                            if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts:
+                                # This will only happen when we force the next token to be the end of thought token
+                                break
+                            dqn_loss_list.append(actor_loss.mean())
+
+        if loss_list:
+            if self.first_and_last_mode:
+                loss = sum(
+                    self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk)
+                ) * (1 - self.original_loss_weight) / self.n_ahead_talk
+                loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight
+                # Let's NaN out the others
+                # e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4
+                for i in range(1, len(loss_list) - self.n_ahead_talk):
+                    loss_list[i] = loss_list[i] * math.nan
+            elif self.first_only:
+                loss = self.loss_mean(loss_list[0])
+            elif self.final_only_mode:
+                loss = sum(
+                    self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1)
+                ) / self.n_ahead_talk   
+            else:
+                loss = None
+                for i in range(len(loss_list)):
+                    cur_loss = self.loss_mean(loss_list[i])
+                    if loss is not None:
+                        loss = loss + cur_loss.to(loss.device)
+                    else:
+                        loss = cur_loss
+                loss = loss / len(loss_list)
+            
+            loss = loss * self.base_loss_beta
+
+        if dqn_loss_list:
+            dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list)
+            if self.include_policy_loss:
+                if loss is not None:
+                    loss += dqn_loss * self.policy_loss_beta
+                else:
+                    loss = dqn_loss * self.policy_loss_beta
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+    
+        base_log_dict = {
+            f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list))
+        }
+
+        if loss is not None:
+            base_log_dict["loss_train"] = loss.item()
+        
+        for loss_key, loss_val in base_log_dict.items():
+            log_dict[loss_key] += loss_val / self.n_tokens_print
+                
+        if self.use_policy_loss and policy_reward is not None:
+            log_dict["policy_loss"] += dqn_loss / self.n_tokens_print
+            log_dict["policy_reward"] += policy_reward.mean() / self.n_tokens_print
+
+        if not loss_list:
+            if loss is not None:
+                log_dict["loss_0"] += loss / self.n_tokens_print
+        else:
+            log_dict["loss_final"] += nonzero_mean(loss_list[-1]) / self.n_tokens_print
+            log_dict["loss_talk"] += sum(nonzero_mean(cur_loss_item) for cur_loss_item in loss_list[-self.n_ahead_talk:]) / self.n_ahead_talk / self.n_tokens_print
+
+        # also log relative losses to loss_0
+        if loss_list:
+            for i in range(len(loss_list)):
+                talk_idx = min(max(i - (self.n_ahead - 1), 0), len(talk_loss_list) - 1)
+                if not talk_loss_list:
+                    cur_talk_loss = nonzero_mean(loss_list[0])
+                else:
+                    cur_talk_loss = talk_loss_list[talk_idx]
+                log_dict[f"rel_loss_{i}"] += (nonzero_mean(loss_list[i]) - cur_talk_loss) / self.n_tokens_print
+        if self.training:
+            self.training_steps += 1
+        try:
+            # if self.training_steps % (self.gradient_accumulation_steps * 256) == 0:
+            if self.wandb_enabled:
+                if self.training_steps % (self.n_tokens_print) == 0 or not self.training:# and "0" in str(loss.device):
+                    if not self.training:
+                        new_log_dict = {}
+                        for key in list(log_dict.keys()):
+                            new_log_dict["eval_" + key] = log_dict[key]
+                        log_dict = new_log_dict
+                    log_dict["training_steps"] = self.training_steps 
+                    log_dict["batch_size"] = batch_size
+                    log_dict["example_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps
+                    if self.n_ahead > 1:
+                        log_dict["compute_steps"] = self.training_steps * batch_size * (self.n_ahead + self.n_ahead_talk - 1) * self.gradient_accumulation_steps
+                    else: # There's no overhead for talk tokens if there's no thinking
+                        log_dict["compute_steps"] = self.training_steps * batch_size * self.gradient_accumulation_steps
+                    # remove all nans
+                    for key in list(log_dict.keys()):
+                        if log_dict[key] != log_dict[key]:
+                            del log_dict[key]
+                    if self.training:
+                        wandb.log(log_dict)
+                    if self.training:
+                        self.log_dict = defaultdict(int)
+                    else:
+                        self.eval_log_dict = defaultdict(int)
+        except Exception as e:
+            pass
+
+        if not self.training:
+            self.n_ahead_talk = n_ahead_talk_to_restore
+            self.n_passes = n_passes_to_restore
+        return CausalLMOutputWithPast(
+            loss=loss if loss is not None else None,
+            logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward_quiet(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, QuietForCausalLM
+
+        >>> model = QuietForCausalLM.from_pretrained("quietai/Quiet-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("quietai/Quiet-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+        hidden_states = outputs.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        thought_ids, thought_embeddings = self._generate_thoughts(hidden_states, max_length=self.config.thought_length)
+        thought_hidden_states = self.model(inputs_embeds=thought_embeddings).last_hidden_state
+
+        # Compute thought logits
+        thought_logits = self.lm_head(thought_hidden_states)
+
+        # Mix base and thought logits
+        mixed_logits = logits.unsqueeze(1) + self.mixing_head(thought_logits)
+        mixed_logits = mixed_logits.view(-1, mixed_logits.size(-1))
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = mixed_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+            if self.use_policy_loss:
+                rewards = loss.detach().unsqueeze(1).repeat(1, self.max_thoughts)
+                if self.remove_negative_rewards:
+                    rewards = torch.clamp(rewards, min=0)
+                policy_loss = self.calculate_policy_loss(thought_ids, rewards)
+                loss = loss + policy_loss
+        else:
+            loss = None
+
+        if not return_dict:
+            output = (mixed_logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss if loss is not None else None,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward_legacy(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Ensure tensors are on the same device
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def self_extend_forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        padding_mask: Optional[torch.LongTensor] = None,
+        group_size_1: Optional[float] = 8,
+        group_size_2: Optional[float] = 2048,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+    
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+    
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    
+        
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+        
+        query_position_ids = position_ids
+        key_position_ids = torch.arange(kv_seq_len, dtype=position_ids.dtype).to(query_position_ids.device).view(bsz, kv_seq_len)
+    
+    
+        neighbor_query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin, query_position_ids) 
+        _, neighbor_key_states = apply_rotary_pos_emb(None, key_states, cos, sin, key_position_ids) 
+        _re_group_size_2 = 0 if position_ids.max() < group_size_2 else group_size_2 # in case that, the smallest q position, g2-g2//g1 exceed the max position
+        group_query_states, _ = apply_grouped_rotary_pos_emb(query_states, None, cos, sin, query_position_ids, g_size_1=group_size_1, g_size_2=_re_group_size_2) 
+        _, group_key_states = apply_grouped_rotary_pos_emb(None, key_states, cos, sin, key_position_ids, g_size_1=group_size_1, g_size_2=_re_group_size_2) 
+    
+    
+        group_key_states = repeat_kv(group_key_states, self.num_key_value_groups)
+        neighbor_key_states = repeat_kv(neighbor_key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+    
+        neighbor_attn_weights = torch.matmul(neighbor_query_states, neighbor_key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        group_attn_weights = torch.matmul(group_query_states, group_key_states.transpose(2, 3)) / math.sqrt(self.head_dim) 
+    
+    
+        if group_attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {group_attn_weights.size()}"
+            )
+        
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            group_attn_weights = group_attn_weights + attention_mask
+            neighbor_attn_weights = neighbor_attn_weights + attention_mask
+    
+    
+        if q_len == 1:
+            neighbor_attention_mask = torch.zeros((q_len, kv_seq_len), device=neighbor_attn_weights.device)
+            neighbor_attention_mask[:, -group_size_2:] = 1
+        elif q_len == kv_seq_len:
+            neighbor_attention_mask = torch.ones((q_len, kv_seq_len), device=neighbor_attn_weights.device)
+            neighbor_attention_mask = torch.tril(neighbor_attention_mask)
+            if q_len-group_size_2 > 0:
+                group_attention_mask =  torch.tril(torch.ones((q_len-group_size_2, kv_seq_len-group_size_2), device=group_attn_weights.device))
+                neighbor_attention_mask[group_size_2:, :-group_size_2] -= group_attention_mask
+    
+        else:
+            raise ValueError("q_len should be 1 or seq_len.")
+    
+    
+        neighbor_attention_mask = neighbor_attention_mask.bool()
+        attn_weights = torch.where(neighbor_attention_mask, neighbor_attn_weights, group_attn_weights)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+    
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+    
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    
+        attn_output = self.o_proj(attn_output)
+    
+        if not output_attentions:
+            attn_weights = None
+    
+        return attn_output, attn_weights, past_key_value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forwardStar(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        log_dict = self.log_dict if self.training else self.eval_log_dict
+
+        if self.training and self.kill_after is not None and self.training_steps // self.gradient_accumulation_steps > self.kill_after:
+            raise ValueError("Killed after")
+
+        if not self.training:
+            n_ahead_talk_to_restore = self.n_ahead_talk
+            n_passes_to_restore = self.n_passes
+            self.n_ahead_talk = 1
+            self.n_passes = 1
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual
+        assert not (self.skip_residual and self.use_policy_loss)
+
+        if self.tokenized_thought_prefix is None and self.use_thought_prefix:
+            self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"]
+
+        def apply_head(head, states, detach=False):
+            if detach:
+                head_weight = head.weight.detach()
+            else:
+                head_weight = head.weight
+            head_weight = head_weight.to(states.device)
+            return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous()
+    
+        def idx_if_sequential(head, idx=0):
+            if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList):
+                return idx_if_sequential(head[idx], idx=idx)
+            return head
+
+        def none_repeat_interleave(x, n):
+            if x is None:
+                return x
+            return x.repeat_interleave(n, dim=0)
+
+        if self.n_passes > 1:
+            input_ids = none_repeat_interleave(input_ids, self.n_passes)
+            attention_mask = none_repeat_interleave(attention_mask, self.n_passes)
+            position_ids = none_repeat_interleave(position_ids, self.n_passes)
+            inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes)
+            labels = none_repeat_interleave(labels, self.n_passes)
+            if past_key_values is not None:
+                past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values]
+        cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device)
+
+        self.tokenizer_has_start_thought_token = True
+        self.tokenizer_has_end_thought_token = True
+        if self.start_token_id is None:
+            self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+            if self.start_token_id == 0:
+                self.start_token_id = self.tokenizer.bos_token_id
+                self.tokenizer_has_start_thought_token = False
+            elif self.use_start_thought_token:
+                # base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token)
+                base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0]
+                if self.initialize_thought_embedding_to_normal:
+                    self.start_embedding.data = torch.zeros_like(self.start_embedding.data)
+                else:
+                    self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale
+                self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
+        if self.end_token_id is None:
+            self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+            if self.end_token_id == 0:
+                self.end_token_id = self.tokenizer.eos_token_id
+                self.tokenizer_has_end_thought_token = False
+            elif self.use_end_thought_token:
+                # base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token)
+                base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0]
+                if self.initialize_thought_embedding_to_normal:
+                    self.end_embedding.data = torch.zeros_like(self.end_embedding.data)
+                else:
+                    self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale
+                self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
+
+        if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode):
+            self.rm_initialized = True                        
+            if not self.use_shallow_talk:
+                head = self.talk_head[0]
+                cur_head = head[-1] if isinstance(head, nn.Sequential) else head
+                talk_input_dim = cur_head.weight.data.shape[1]
+                talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0]
+                cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype)
+            else:
+                # convert to identity transform
+                def lambda_transform(cur_head):
+                    if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]:
+                        return torch.cat([
+                        torch.eye(
+                            cur_head.weight.data.shape[0],
+                            device=cur_head.weight.device,
+                            dtype=cur_head.weight.dtype
+                        ),
+                        torch.zeros(
+                            cur_head.weight.data.shape[0],
+                            cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0],
+                            device=cur_head.weight.device,
+                            dtype=cur_head.weight.dtype
+                        )], dim=1)
+                    return torch.eye(
+                        cur_head.weight.data.shape[0],
+                        device=cur_head.weight.device,
+                        dtype=cur_head.weight.dtype
+                    )
+                if isinstance(self.talk_head[0], nn.Sequential):
+                    for cur_head in self.talk_head[0]:
+                        # if it has weights
+                        if hasattr(cur_head, "weight"):
+                            cur_head.weight.data = lambda_transform(cur_head)
+                else:
+                    self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0])
+
+        loss = None
+        prev_rm_tokens = None
+        cur_rm_tokens = None
+        prev_rm_logits = None
+        prev_sample_probs = None
+        did_skip_sampling = None
+        skip_sampling = None
+        sample_probs = None
+        hidden_states = None
+        logits = None
+        talk_kl_penalty = None
+        rm_logits = None
+        residual_logits = None
+        probabilities_2d = None
+        prev_probabilities_2d = None
+        policy_reward = None
+        logits_to_output = None
+        batch_size, seq_len = input_ids.shape
+        base_input_ids = input_ids.clone()
+        loss_list = []
+        dqn_loss_list = []
+        sampled_token_history = []
+        sample_probs_history = []
+        action_loglikelihoods_list = []
+
+        if self.use_end_thought_token or self.use_start_thought_token:
+            if not self.use_reparam_for_thought_embeddings:
+                start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale
+                end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale
+            else:
+                start_embedding = self.start_embedding * self.embedding_scale
+                end_embedding = self.end_embedding * self.embedding_scale
+            base_embeddings = self.model.embed_tokens.weight
+            if self.train_only_thinking_embedding:
+                base_embeddings = base_embeddings.detach()
+        # # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1
+        for ahead_idx in range(fwd_iters):
+            past_key_values_length = 0
+            if past_key_values is not None:
+                use_legacy_cache = not isinstance(past_key_values, Cache)
+                if use_legacy_cache:
+                    past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                past_key_values_length = past_key_values.get_usable_length(seq_len)
+
+            if position_ids is None:
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+                position_ids = torch.arange(
+                    past_key_values_length, seq_len + past_key_values_length, dtype=torch.long, device=device
+                )
+                position_ids = position_ids.unsqueeze(0).view(-1, seq_len)
+            else:
+                position_ids = position_ids.view(-1, seq_len).long()
+
+            if inputs_embeds is None:
+                contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any()
+                contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any()
+                contains_thought = contains_start or contains_end
+                if contains_thought:
+                    thought_id = self.start_token_id if contains_start else self.end_token_id
+                    cur_thought_embedding = start_embedding if contains_start else end_embedding
+                    if self.use_reparam_for_thought_embeddings:
+                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
+                        inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
+                        if contains_start:
+                            sampled_start = inputs_embeds.clone().detach()
+                        if contains_end:
+                            sampled_end = inputs_embeds.clone().detach()
+                    else:
+                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
+                else:
+                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
+                        inputs_embeds = self.model.embed_tokens(input_ids)
+            
+            if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode:
+                if attention_mask is None:
+                    base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device)
+                    base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len)
+                    base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1)
+                    attention_mask = base_attention_mask
+                    breakpoint()
+                elif attention_mask.dim() == 2:
+                    if seq_len + past_key_values_length != attention_mask.shape[-1]:
+                        breakpoint()
+                        attention_mask = torch.cat(
+                            [torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask],
+                            dim=-1
+                        )
+                    # # if the attention mask 
+                    attention_mask = _prepare_4d_causal_attention_mask(
+                        attention_mask,
+                        (batch_size, seq_len),
+                        inputs_embeds,
+                        past_key_values_length,
+                        sliding_window=self.config.sliding_window,
+                    )
+
+            outputs = self.model(
+                # input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            prev_hidden_states = hidden_states
+            hidden_states = outputs[0]
+            prev_rm_logits = rm_logits  # for policy gradient
+            prev_rm_tokens = cur_rm_tokens  # for policy gradient
+
+            if ahead_idx == 0:
+                hidden_states_lm = hidden_states
+                logits = self.lm_head(hidden_states_lm)
+                base_hidden_states = hidden_states.clone()
+                initial_loss_logits = logits.clone()
+                if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start:
+                    logits = logits.detach()
+                    base_hidden_states = base_hidden_states.detach()
+                if self.optimize_model_only_at_start:
+                    hidden_states = hidden_states.detach()
+                base_logits = logits.clone()
+            else:
+                talk_hidden_states = hidden_states
+                if self.merged_lm_and_talk_heads:
+                    assert self.no_residual
+                    residual_logits = self.lm_head(hidden_states)
+                    talk_hidden_states = hidden_states
+                else:
+                    if ahead_idx > self.n_ahead - 1:
+                        cur_base_hidden = torch.cat([
+                            base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                    else:
+                        cur_base_hidden = base_hidden_states
+
+                    if self.use_concat_talk_head:
+                        # concatenate the hidden states with the original hidden states
+                        head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1)
+                    else:
+                        head_input_hidden_states = talk_hidden_states
+
+                    residual_logits = self.talk_head[0](head_input_hidden_states)
+                    if self.use_shallow_talk:
+                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)                        
+                    residual_logits = residual_logits.to(logits.device)
+                    if self.use_weighted_talk_head:
+                        # combine the cur_base_hidden with the talk_hidden_states according to the weighted head
+                        residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits
+                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)
+
+                assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1
+                if self.clever_residual:
+                    if ahead_idx >= self.n_ahead - 1:
+                        # get the logits shifted according to the current talk ahead
+                        cur_base_logits = torch.cat([
+                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                        if self.optimize_lm_head_only_at_start:
+                            cur_base_logits = cur_base_logits.detach()
+                        logits = cur_base_logits + residual_logits
+                    else:
+                        logits += residual_logits / self.n_ahead
+                elif self.cumulative_residual:
+                    if self.residual_talk_head:
+                        if ahead_idx < self.n_ahead:
+                            logits += residual_logits
+                        else:
+                            # get the logits shifted according to the current talk ahead
+                            cur_base_logits = torch.cat([
+                                base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                                base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                            ], dim=-2)
+                            if self.optimize_lm_head_only_at_start:
+                                cur_base_logits = cur_base_logits.detach()
+                            logits = cur_base_logits + residual_logits
+                    else:
+                        if ahead_idx < self.n_ahead:
+                            logits += residual_logits
+                        else:
+                            logits = residual_logits
+                elif self.skip_residual:
+                    if ahead_idx >= self.n_ahead:
+                        # get the logits shifted according to the current talk ahead
+                        cur_base_logits = torch.cat([
+                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                        if self.optimize_lm_head_only_at_start:
+                            cur_base_logits = cur_base_logits.detach()
+                        logits = cur_base_logits
+                elif self.no_residual:
+                    logits = residual_logits
+                else:
+                    logits = base_logits + residual_logits
+
+            attempted = False
+            talk_loss_list = []
+            if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0):
+                loss = None
+                attempted = True
+
+                if labels is not None:
+                    for shift_amount in range(self.n_ahead_talk):
+                        # Shift so that tokens < n predict n
+                        #  ab[cde]f
+                        # abc[def]
+                        if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
+                            loss_logits = initial_loss_logits
+                        else:
+                            loss_logits = logits
+                        shift_logits = loss_logits[..., shift_amount:-1, :].contiguous()
+                        shift_labels = labels[..., 1 + shift_amount:].contiguous()
+                        # Flatten the tokens
+                        loss_fct = CrossEntropyLoss(reduction="none")
+                        shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                        shift_labels = shift_labels.view(-1).clone()
+                        # Enable model parallelism
+                        shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100
+                        shift_labels = shift_labels.to(shift_logits.device)
+                        loss = loss_fct(shift_logits, shift_labels)
+                        if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode:
+                            loss_list.append(loss)
+                        talk_loss_list.append(nonzero_mean(loss).detach())
+            
+            if not attempted or self.comparison_mode:
+                rm_hidden_states = hidden_states
+                # print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm())
+                rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start)
+                    
+                # don't allow it to predict the thinking token
+                if self.tokenizer_has_start_thought_token:                    
+                    rm_logits[..., self.start_token_id] = -1e10
+                if self.tokenizer_has_end_thought_token:
+                    rm_logits[..., self.end_token_id] = -1e10
+                probabilities = rm_logits
+                if probabilities_2d is not None:
+                    prev_probabilities_2d = probabilities_2d.clone()
+                probabilities_2d = probabilities.view(-1, probabilities.size(-1))
+
+                did_skip_sampling = skip_sampling
+                skip_sampling = False
+                if ahead_idx == 0 and self.use_start_thought_token:
+                    override_token = self.start_token_id
+                elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]:
+                    override_token = self.tokenized_thought_prefix[..., ahead_idx]
+                elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token:
+                    override_token = self.end_token_id
+                else:
+                    override_token = None
+                if override_token is not None and self.n_ahead > 1:
+                    # always start with the start token
+                    probabilities_2d = torch.zeros_like(probabilities_2d)
+                    probabilities_2d[:, override_token] = 1.0
+                    skip_sampling = True
+                elif ahead_idx >= self.n_ahead - 1:
+                    if labels is not None:  # we're in the talk phase
+                        cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1
+                        # print("Setting rm to labels", cur_talk_n, "during", ahead_idx)
+                        shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device)
+                        padding = torch.full_like(
+                            labels[..., :cur_talk_n],
+                            self.tokenizer.pad_token_id,
+                            dtype=torch.long,
+                            device=shift_labels.device
+                        )
+                        new_rm_tokens = torch.cat(
+                            [shift_labels, padding],
+                            dim=-1
+                        )
+                        # convert rm tokens to one-hot
+                        probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype)
+                        skip_sampling = True
+                    else:
+                        continue
+                temperature = self.gumbel_temperature if self.training else 0.001
+                prev_sample_probs = sample_probs
+                sample_probs = probabilities_2d
+                if ahead_idx < self.n_ahead - 1 and not skip_sampling:
+                    probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1)
+                    if self.gumbel_detach:
+                        probabilities_2d = probabilities_2d.detach()
+                sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu())
+                # convert rm logits directly to embeddings
+                contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0)
+                contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0)
+                contains_thought = contains_start or contains_end
+
+                if not contains_thought:
+                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
+                        inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype))
+                else:
+                    thought_id = self.start_token_id if contains_start else self.end_token_id
+                    cur_thought_embedding = start_embedding if contains_start else end_embedding
+                    if self.use_reparam_for_thought_embeddings:
+                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
+                        inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
+                        if contains_start:
+                            sampled_start = inputs_embeds.clone().detach()
+                        else:
+                            sampled_end = inputs_embeds.clone().detach()
+                    else:
+                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
+                        inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
+                inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
+
+                if len(attention_mask.shape) == 2:
+                    breakpoint()
+                else:
+                    original_attention = attention_mask[..., :attention_mask.shape[-2]]
+                    if self.use_upper_triangular:
+                        new_attention = original_attention
+                    else:
+                        original_attention = original_attention == attention_mask.max()
+                        # because eye isn't implemented for BF16, we need to handle the case
+                        if not attention_mask.dtype == torch.bfloat16:
+                            new_attention = torch.eye(
+                                seq_len, dtype=attention_mask.dtype, device=attention_mask.device
+                            )
+                        else:
+                            new_attention = torch.eye(
+                                seq_len, dtype=torch.float32, device=attention_mask.device
+                            ).to(attention_mask.dtype)
+
+                        new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1)
+                        new_attention = new_attention * original_attention
+                        new_attention[new_attention == 0] = attention_mask.min()
+                        new_attention[new_attention == 1] = attention_mask.max()
+                    attention_mask = torch.cat([attention_mask, new_attention], dim=-1)
+                past_key_values = outputs.past_key_values
+                position_ids = position_ids + 1
+
+                if labels is not None and (self.n_ahead > 1 or not self.base_original_mode):
+                    # Shift so that tokens < n predict n
+                    # logits: abcdef -> bcdef? -> cdef??
+                    # labels: abcdef -> ?bcdef -> ??cdef
+                    if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
+                        loss_logits = initial_loss_logits
+                    else:
+                        loss_logits = logits
+                    shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1))
+                    shift_logits = loss_logits[..., :-shift_idx, :].contiguous()
+                    shift_labels = labels[..., shift_idx:].contiguous()
+                    # Flatten the tokens
+                    loss_fct = CrossEntropyLoss(reduction="none")
+                    shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                    shift_labels = shift_labels.view(-1)
+                    # Enable model parallelism
+                    shift_labels = shift_labels.to(shift_logits.device)
+                    # if shift_labels.min() == self.tokenizer.pad_token_id:
+                    shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels)
+                    unreduced_loss = loss_fct(shift_logits, shift_labels)
+                    if torch.any(unreduced_loss != unreduced_loss):
+                        raise ValueError("NaN loss")
+                    unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1)
+                    loss_list.append(unreduced_loss)
+
+
+                    if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token):
+                        # we treat the change in loss as the reward
+                        previous_loss = loss_list[-2]
+                        # for example, suppose n_ahead = 3 and n_ahead_talk = 2
+                        # note that we end at self.n_ahead + self.n_ahead_talk - 2
+                        # in this case, 5 - 2 = 3, so we end at ahead_idx = 3
+                        # we also predict the next token at ahead_idx = 2
+                        # when we get to ahead_idx = 2, we predict ahead
+                        # so we shift by 1
+                        # note that this is ahead_idx = n_ahead - 1
+                        # when we get to ahead_idx = 3, we predict ahead
+                        # so we shift by 2
+                        # note that this is ahead_idx = n_ahead
+                        if ahead_idx < self.n_ahead - 1:
+                            shift_amount = 0
+                            original_dqn_reward = (previous_loss - unreduced_loss).detach()
+                            if self.first_and_last_mode:
+                                original_dqn_reward = original_dqn_reward * 0.0
+                        else:
+                            # logits vs cur_policy_shift_logits
+                            # let's look at rm_logits and prev_rm_logits
+                            shift_amount = max(0, ahead_idx - (self.n_ahead - 1))
+                            # let's say shift_amount = 2
+                            # abcdefg -> bcdefg? -> cdefg??
+                            # logits = [a b]c d e f[g]
+                            # labels = [a b c]d e f g
+                            cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach()
+                            cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous()
+                            # Flatten the tokens
+                            cur_policy_loss_fct = CrossEntropyLoss(reduction="none")
+                            cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size)
+                            cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone()
+                            # Enable model parallelism
+                            cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100
+                            cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device)
+                            cur_policy_reward_base_loss = loss_fct(
+                                cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device)
+                            ).reshape(logits.shape[0], -1)
+                            original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss
+                                
+                        if not did_skip_sampling:
+                            nonzero_indices = prev_probabilities_2d.nonzero()
+                            action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]]
+                            action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount]
+                            action_loglikelihoods_list.append(action_loglikelihoods_2d)
+                        if policy_reward is None:
+                            policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
+                        else:
+                            if self.n_ahead_talk > shift_amount:
+                                added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
+                            else:
+                                added_reward = original_dqn_reward
+                            policy_reward += added_reward
+                    
+                    if self.use_policy_loss and ahead_idx == self.n_ahead + self.n_ahead_talk - 2:
+                        # only compute during the thinking phase
+                        if self.use_reparam_for_thought_embeddings and (self.use_start_thought_token or self.use_end_thought_token):
+                            # sampled_start, sampled_end
+                            # calculate the log likelihood of the start and end embeddings sampled from a multivariate normal distribution
+                            # with mean start_embedding[0] and standard deviation start_embedding[1]
+                            if self.use_start_thought_token:
+                                exp_start_std = torch.exp(start_embedding[1])
+                                start_loglikelihood = -0.5 * (sampled_start.detach() - start_embedding[0]) ** 2 / exp_start_std ** 2 - start_embedding[1] - 0.5 * math.log(2 * math.pi)
+                                start_loglikelihood = start_loglikelihood.mean(dim=-1)
+                            if self.use_end_thought_token:
+                                exp_end_std = torch.exp(end_embedding[1])
+                                end_loglikelihood = -0.5 * (sampled_end.detach() - end_embedding[0]) ** 2 / exp_end_std ** 2 - end_embedding[1] - 0.5 * math.log(2 * math.pi)
+                                end_loglikelihood = end_loglikelihood.mean(dim=-1)
+                            # we use the mean instead of the sum to prevent dependence on the dimensionality of the embeddings
+                            if self.use_end_thought_token and self.use_policy_loss_for_end_thought:
+                                action_loglikelihoods_list.append(end_loglikelihood)
+                            if self.use_start_thought_token:
+                                action_loglikelihoods_list.append(start_loglikelihood)                                
+
+                        if ahead_idx == self.n_ahead + self.n_ahead_talk - 2 and self.eval_mode:
+                            with torch.no_grad():
+                                # calculate the 0.75 quantile of the rewards
+                                filtered_tokens = input_ids[:, :policy_reward.shape[-1]].cpu().detach().numpy().flatten()
+                                filtered_tokens_mask = filtered_tokens != self.tokenizer.pad_token_id
+                                filtered_tokens = filtered_tokens[filtered_tokens_mask]
+                                filtered_rewards = policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten()
+                                filtered_rewards = filtered_rewards[filtered_tokens_mask]
+
+                                abs_reward_list = np.abs(policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten())
+                                abs_reward_list = abs_reward_list[filtered_tokens_mask]
+                                medium_quantile = np.quantile(abs_reward_list, 0.5)
+                                upper_quantile = np.quantile(abs_reward_list, 0.95)
+
+                        for action_loglikelihoods_2d in action_loglikelihoods_list:
+                            train_policy_reward = policy_reward
+
+                            # discard rewards below the mean
+                            if self.trice_mode and self.n_passes > 1:
+                                batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1])
+                                # average over the passes
+                                train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True)
+                                train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1])
+                                
+                            if self.subtract_mean_reward:
+                                train_policy_reward = train_policy_reward - train_policy_reward.mean()
+                            if self.remove_negative_rewards:
+                                fixed_policy_reward = train_policy_reward.detach().clamp(min=0)
+                            else:
+                                fixed_policy_reward = train_policy_reward.detach()
+                            actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device)
+                            if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts:
+                                # This will only happen when we force the next token to be the end of thought token
+                                break
+                            dqn_loss_list.append(actor_loss.mean())
+
+        if loss_list:
+            if self.first_and_last_mode:
+                loss = sum(
+                    self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk)
+                ) * (1 - self.original_loss_weight) / self.n_ahead_talk
+                loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight
+                # Let's NaN out the others
+                # e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4
+                for i in range(1, len(loss_list) - self.n_ahead_talk):
+                    loss_list[i] = loss_list[i] * math.nan
+            elif self.first_only:
+                loss = self.loss_mean(loss_list[0])
+            elif self.final_only_mode:
+                loss = sum(
+                    self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1)
+                ) / self.n_ahead_talk   
+            else:
+                loss = None
+                for i in range(len(loss_list)):
+                    cur_loss = self.loss_mean(loss_list[i])
+                    if loss is not None:
+                        loss = loss + cur_loss.to(loss.device)
+                    else:
+                        loss = cur_loss
+                loss = loss / len(loss_list)
+            
+            loss = loss * self.base_loss_beta
+
+        if dqn_loss_list:
+            dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list)
+            if self.include_policy_loss:
+                if loss is not None:
+                    loss += dqn_loss * self.policy_loss_beta
+                else:
+                    loss = dqn_loss * self.policy_loss_beta
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+    
+        base_log_dict = {
+            f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list))
+        }
+
+        if loss is not None:
+            base_log_dict["loss_train"] = loss.item()
+        
+        for loss_key, loss_val in base_log_dict.items():
+            log_dict[loss_key] += loss_val / self.n_tokens_print
+                
+        if self.use_policy_loss and policy_reward is not None:
+            log_dict["policy_loss"] += dqn_loss / self.n_tokens_print
+            log_dict["policy_reward"] += policy_reward.mean() / self.n_tokens_print
+
+        if not loss_list:
+            if loss is not None:
+                log_dict["loss_0"] += loss / self.n_tokens_print
+        else:
+            log_dict["loss_final"] += nonzero_mean(loss_list[-1]) / self.n_tokens_print
+            log_dict["loss_talk"] += sum(nonzero_mean(cur_loss_item) for cur_loss_item in loss_list[-self.n_ahead_talk:]) / self.n_ahead_talk / self.n_tokens_print
+
+        # also log relative losses to loss_0
+        if loss_list:
+            for i in range(len(loss_list)):
+                talk_idx = min(max(i - (self.n_ahead - 1), 0), len(talk_loss_list) - 1)
+                if not talk_loss_list:
+                    cur_talk_loss = nonzero_mean(loss_list[0])
+                else:
+                    cur_talk_loss = talk_loss_list[talk_idx]
+                log_dict[f"rel_loss_{i}"] += (nonzero_mean(loss_list[i]) - cur_talk_loss) / self.n_tokens_print
+        if self.training:
+            self.training_steps += 1
+
+        if not self.training:
+            self.n_ahead_talk = n_ahead_talk_to_restore
+            self.n_passes = n_passes_to_restore
+        return CausalLMOutputWithPast(
+            loss=loss if loss is not None else None,
+            logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+class MistralSelfExtendForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MistralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.max_thoughts = config.max_thoughts
+        self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads
+        self.use_concat_talk_head = config.use_concat_talk_head
+        self.use_shallow_talk = config.use_shallow_talk
+        self.use_complex_talk_head = config.use_complex_talk_head
+        self.use_weighted_talk_head = config.use_weighted_talk_head
+        # the weighted head will output a single value, so it can't be passed to the lm head
+        assert not (self.use_weighted_talk_head and self.use_shallow_talk)
+
+        self.n_ahead = 1
+        self.n_ahead_talk = 1
+        self.n_passes = 1
+        self.n_tokens_print = 1
+        self.gradient_accumulation_steps = 1
+        self.training_steps = 0
+        self.tokenizer = None
+        self.start_token_id = None
+        self.end_token_id = None
+        self.rm_initialized = False
+        self.residual_talk_head = True
+        self.thought_init_std_scale = 1e-2
+
+        self.final_only_mode = False
+        self.first_and_last_mode = True
+        self.first_only = False
+        self.original_loss_weight = 0.5
+
+        self.cumulative_residual = False
+        self.clever_residual = False
+        self.skip_residual = False
+        self.no_residual = True
+
+        self.optimize_lm_head_only_at_start = False
+        self.optimize_model_only_at_start = False
+
+        if self.optimize_model_only_at_start:
+            raise NotImplementedError
+        self.train_only_thinking_embedding = False
+        self.weighted_embeddings = False
+        self.use_start_thought_token = True
+        self.use_end_thought_token = True
+        self.initialize_thought_embedding_to_normal = False
+        self.initial_start_token = "---"
+        self.initial_end_token = "---"
+        self.output_logits_at_the_end = True
+
+        self.gumbel_temperature = 0.001
+
+        self.use_policy_loss = True
+        self.include_policy_loss = True
+        self.trice_mode = True
+        self.remove_negative_rewards = True
+        self.use_policy_loss_for_end_thought = True
+        
+        self.base_original_mode = False
+        self.original_mode = False
+
+        self.thought_prefix = "(Let's think step by step"
+        self.tokenized_thought_prefix = None
+        self.log_dict = defaultdict(int)
+        self.eval_log_dict = defaultdict(int)
+        self.print_final_only = True
+        self.loss_mean = loss_mean
+        self.all_rewards = []
+        self.all_unreduced_losses = []
+        self.kill_after = 100
+
+        self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+        self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+
+        self.policy_loss_beta = 1e6
+        self.embedding_scale = 1e2
+        self.reinforce_temperature = 3
+        self.base_loss_beta = 1
+
+        # Not used in the paper:
+        self.use_thought_prefix = False
+        self.use_reparam_for_thought_embeddings = False
+        self.use_upper_triangular = False
+        self.subtract_mean_reward = False
+        self.comparison_mode = False
+        self.gumbel_detach = True
+    
+        # For visualization
+        self.eval_mode = False
+
+        num_talk = 1
+        talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
+        if self.use_weighted_talk_head:
+            talk_output_dim = 1
+        else:
+            talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size
+
+        if not self.merged_lm_and_talk_heads:
+            if self.use_complex_talk_head:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, talk_output_dim, bias=False)
+                )])
+            else:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, talk_output_dim, bias=False)
+                )])
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+    def calculate_policy_loss(self, thoughts, rewards):
+        thought_log_probs = []
+        for thought in thoughts:
+            thought_log_prob = self.lm_head(thought).log_softmax(dim=-1)
+            thought_log_probs.append(thought_log_prob)
+        
+        thought_log_probs = torch.stack(thought_log_probs, dim=1)  # (batch_size, num_thoughts, seq_length, vocab_size)
+        thought_probs = torch.exp(thought_log_probs)
+        
+        policy_loss = -torch.mean(thought_log_probs * rewards.unsqueeze(-1).unsqueeze(-1))
+        
+        return policy_loss
+    
+    def _generate_thoughts(self, hidden_states, max_length):
+        batch_size = hidden_states.size(0)
+        thought_ids = torch.zeros((batch_size, self.config.max_thoughts, max_length), dtype=torch.long, device=hidden_states.device)
+        thought_embeddings = []
+
+        for i in range(self.config.max_thoughts):
+            thought_input_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=hidden_states.device)
+            thought_outputs = self.generate(
+                input_ids=thought_input_ids,
+                max_length=max_length,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                pad_token_id=self.config.pad_token_id,
+                eos_token_id=self.config.eos_token_id,
+            )
+            thought_ids[:, i, :] = thought_outputs
+            thought_embeddings.append(self.get_input_embeddings()(thought_outputs))
+
+        thought_embeddings = torch.stack(thought_embeddings, dim=1)
+        return thought_ids, thought_embeddings
+
+    @torch.no_grad()
+    def infer(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        batch_size, seq_len = input_ids.shape
+
+        # Save the original input_ids and attention_mask for later use
+        original_input_ids = input_ids.clone()
+        original_attention_mask = attention_mask.clone() if attention_mask is not None else None
+
+        # Append the start thought token to the input sequence
+        start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Generate the continuation
+        continuation_length = self.n_ahead - 2
+        new_key_values = past_key_values
+        
+        start_time = time.time()
+        for continuation_idx in range(continuation_length):
+            outputs = self.model(
+                input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device),
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=new_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            new_key_values = outputs.past_key_values
+
+            hidden_states = outputs[0]
+
+            logits = self.lm_head(hidden_states)
+            logits = logits[:, -1, :]  # Only consider the last token
+
+            # Apply Gumbel-Softmax to the logits
+            next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1)
+            next_token_id = torch.argmax(next_token_logits, dim=-1)
+
+            # Append the generated token to the input sequence
+            input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1)
+            seq_len += 1
+
+            # Update the attention mask
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Append the end thought token to the input sequence
+        end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Get the hidden states before and after the thought
+        outputs_before = self.model(
+            input_ids=original_input_ids,
+            attention_mask=original_attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_before = outputs_before[0][:, -1:, :]
+
+        # two new tokens: last continuation token and end thought token
+        outputs_after = self.model(
+            input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=new_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_after = outputs_after[0][:, -1:, :]
+
+        # Apply the talk head to get the mixing weight
+        mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1))
+
+        # Apply the mixing weight to the hidden states
+        mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after
+
+        # Apply the language model head to get the final logits
+        logits = self.lm_head(mixed_hidden_states)
+        return logits
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        padding_mask: Optional[torch.LongTensor] = None,
+        group_size_1: Optional[float] = 8,
+        group_size_2: Optional[float] = 2048,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+    
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+    
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+    
+        
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+        
+        query_position_ids = position_ids
+        key_position_ids = torch.arange(kv_seq_len, dtype=position_ids.dtype).to(query_position_ids.device).view(bsz, kv_seq_len)
+    
+    
+        neighbor_query_states, _ = apply_rotary_pos_emb(query_states, None, cos, sin, query_position_ids) 
+        _, neighbor_key_states = apply_rotary_pos_emb(None, key_states, cos, sin, key_position_ids) 
+        _re_group_size_2 = 0 if position_ids.max() < group_size_2 else group_size_2 # in case that, the smallest q position, g2-g2//g1 exceed the max position
+        group_query_states, _ = apply_grouped_rotary_pos_emb(query_states, None, cos, sin, query_position_ids, g_size_1=group_size_1, g_size_2=_re_group_size_2) 
+        _, group_key_states = apply_grouped_rotary_pos_emb(None, key_states, cos, sin, key_position_ids, g_size_1=group_size_1, g_size_2=_re_group_size_2) 
+    
+    
+        group_key_states = repeat_kv(group_key_states, self.num_key_value_groups)
+        neighbor_key_states = repeat_kv(neighbor_key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+    
+        neighbor_attn_weights = torch.matmul(neighbor_query_states, neighbor_key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        group_attn_weights = torch.matmul(group_query_states, group_key_states.transpose(2, 3)) / math.sqrt(self.head_dim) 
+    
+    
+        if group_attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {group_attn_weights.size()}"
+            )
+        
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            group_attn_weights = group_attn_weights + attention_mask
+            neighbor_attn_weights = neighbor_attn_weights + attention_mask
+    
+    
+        if q_len == 1:
+            neighbor_attention_mask = torch.zeros((q_len, kv_seq_len), device=neighbor_attn_weights.device)
+            neighbor_attention_mask[:, -group_size_2:] = 1
+        elif q_len == kv_seq_len:
+            neighbor_attention_mask = torch.ones((q_len, kv_seq_len), device=neighbor_attn_weights.device)
+            neighbor_attention_mask = torch.tril(neighbor_attention_mask)
+            if q_len-group_size_2 > 0:
+                group_attention_mask =  torch.tril(torch.ones((q_len-group_size_2, kv_seq_len-group_size_2), device=group_attn_weights.device))
+                neighbor_attention_mask[group_size_2:, :-group_size_2] -= group_attention_mask
+    
+        else:
+            raise ValueError("q_len should be 1 or seq_len.")
+    
+    
+        neighbor_attention_mask = neighbor_attention_mask.bool()
+        attn_weights = torch.where(neighbor_attention_mask, neighbor_attn_weights, group_attn_weights)
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+    
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+    
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    
+        attn_output = self.o_proj(attn_output)
+    
+        if not output_attentions:
+            attn_weights = None
+    
+        return attn_output, attn_weights, past_key_value
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+class MistralStarForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MistralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.max_thoughts = config.max_thoughts
+        self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads
+        self.use_concat_talk_head = config.use_concat_talk_head
+        self.use_shallow_talk = config.use_shallow_talk
+        self.use_complex_talk_head = config.use_complex_talk_head
+        self.use_weighted_talk_head = config.use_weighted_talk_head
+        # the weighted head will output a single value, so it can't be passed to the lm head
+        assert not (self.use_weighted_talk_head and self.use_shallow_talk)
+
+        self.n_ahead = 1
+        self.n_ahead_talk = 1
+        self.n_passes = 1
+        self.n_tokens_print = 1
+        self.gradient_accumulation_steps = 1
+        self.training_steps = 0
+        self.tokenizer = None
+        self.start_token_id = None
+        self.end_token_id = None
+        self.rm_initialized = False
+        self.residual_talk_head = True
+        self.thought_init_std_scale = 1e-2
+
+        self.final_only_mode = False
+        self.first_and_last_mode = True
+        self.first_only = False
+        self.original_loss_weight = 0.5
+
+        self.cumulative_residual = False
+        self.clever_residual = False
+        self.skip_residual = False
+        self.no_residual = True
+
+        self.optimize_lm_head_only_at_start = False
+        self.optimize_model_only_at_start = False
+
+        if self.optimize_model_only_at_start:
+            raise NotImplementedError
+        self.train_only_thinking_embedding = False
+        self.weighted_embeddings = False
+        self.use_start_thought_token = True
+        self.use_end_thought_token = True
+        self.initialize_thought_embedding_to_normal = False
+        self.initial_start_token = "---"
+        self.initial_end_token = "---"
+        self.output_logits_at_the_end = True
+
+        self.gumbel_temperature = 0.001
+
+        self.use_policy_loss = True
+        self.include_policy_loss = True
+        self.trice_mode = True
+        self.remove_negative_rewards = True
+        self.use_policy_loss_for_end_thought = True
+        
+        self.base_original_mode = False
+        self.original_mode = False
+
+        self.thought_prefix = "(Let's think step by step"
+        self.tokenized_thought_prefix = None
+        self.log_dict = defaultdict(int)
+        self.eval_log_dict = defaultdict(int)
+        self.print_final_only = True
+        self.loss_mean = loss_mean
+        self.all_rewards = []
+        self.all_unreduced_losses = []
+        self.kill_after = 100
+
+        self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+        self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+
+        self.policy_loss_beta = 1e6
+        self.embedding_scale = 1e2
+        self.reinforce_temperature = 3
+        self.base_loss_beta = 1
+
+        # Not used in the paper:
+        self.use_thought_prefix = False
+        self.use_reparam_for_thought_embeddings = False
+        self.use_upper_triangular = False
+        self.subtract_mean_reward = False
+        self.comparison_mode = False
+        self.gumbel_detach = True
+    
+        # For visualization
+        self.eval_mode = False
+
+        num_talk = 1
+        talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
+        if self.use_weighted_talk_head:
+            talk_output_dim = 1
+        else:
+            talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size
+
+        if not self.merged_lm_and_talk_heads:
+            if self.use_complex_talk_head:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, talk_output_dim, bias=False)
+                )])
+            else:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, talk_output_dim, bias=False)
+                )])
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+    def calculate_policy_loss(self, thoughts, rewards):
+        thought_log_probs = []
+        for thought in thoughts:
+            thought_log_prob = self.lm_head(thought).log_softmax(dim=-1)
+            thought_log_probs.append(thought_log_prob)
+        
+        thought_log_probs = torch.stack(thought_log_probs, dim=1)  # (batch_size, num_thoughts, seq_length, vocab_size)
+        thought_probs = torch.exp(thought_log_probs)
+        
+        policy_loss = -torch.mean(thought_log_probs * rewards.unsqueeze(-1).unsqueeze(-1))
+        
+        return policy_loss
+    
+    def _generate_thoughts(self, hidden_states, max_length):
+        batch_size = hidden_states.size(0)
+        thought_ids = torch.zeros((batch_size, self.config.max_thoughts, max_length), dtype=torch.long, device=hidden_states.device)
+        thought_embeddings = []
+
+        for i in range(self.config.max_thoughts):
+            thought_input_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=hidden_states.device)
+            thought_outputs = self.generate(
+                input_ids=thought_input_ids,
+                max_length=max_length,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                pad_token_id=self.config.pad_token_id,
+                eos_token_id=self.config.eos_token_id,
+            )
+            thought_ids[:, i, :] = thought_outputs
+            thought_embeddings.append(self.get_input_embeddings()(thought_outputs))
+
+        thought_embeddings = torch.stack(thought_embeddings, dim=1)
+        return thought_ids, thought_embeddings
+
+    @torch.no_grad()
+    def infer(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        batch_size, seq_len = input_ids.shape
+
+        # Save the original input_ids and attention_mask for later use
+        original_input_ids = input_ids.clone()
+        original_attention_mask = attention_mask.clone() if attention_mask is not None else None
+
+        # Append the start thought token to the input sequence
+        start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Generate the continuation
+        continuation_length = self.n_ahead - 2
+        new_key_values = past_key_values
+        
+        start_time = time.time()
+        for continuation_idx in range(continuation_length):
+            outputs = self.model(
+                input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device),
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=new_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            new_key_values = outputs.past_key_values
+
+            hidden_states = outputs[0]
+
+            logits = self.lm_head(hidden_states)
+            logits = logits[:, -1, :]  # Only consider the last token
+
+            # Apply Gumbel-Softmax to the logits
+            next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1)
+            next_token_id = torch.argmax(next_token_logits, dim=-1)
+
+            # Append the generated token to the input sequence
+            input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1)
+            seq_len += 1
+
+            # Update the attention mask
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Append the end thought token to the input sequence
+        end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Get the hidden states before and after the thought
+        outputs_before = self.model(
+            input_ids=original_input_ids,
+            attention_mask=original_attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_before = outputs_before[0][:, -1:, :]
+
+        # two new tokens: last continuation token and end thought token
+        outputs_after = self.model(
+            input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=new_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_after = outputs_after[0][:, -1:, :]
+
+        # Apply the talk head to get the mixing weight
+        mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1))
+
+        # Apply the mixing weight to the hidden states
+        mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after
+
+        # Apply the language model head to get the final logits
+        logits = self.lm_head(mixed_hidden_states)
+        return logits
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward_quiet(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, QuietForCausalLM
+
+        >>> model = QuietForCausalLM.from_pretrained("quietai/Quiet-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("quietai/Quiet-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+        hidden_states = outputs.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        thought_ids, thought_embeddings = self._generate_thoughts(hidden_states, max_length=self.config.thought_length)
+        thought_hidden_states = self.model(inputs_embeds=thought_embeddings).last_hidden_state
+
+        # Compute thought logits
+        thought_logits = self.lm_head(thought_hidden_states)
+
+        # Mix base and thought logits
+        mixed_logits = logits.unsqueeze(1) + self.mixing_head(thought_logits)
+        mixed_logits = mixed_logits.view(-1, mixed_logits.size(-1))
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = mixed_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+            if self.use_policy_loss:
+                rewards = loss.detach().unsqueeze(1).repeat(1, self.max_thoughts)
+                if self.remove_negative_rewards:
+                    rewards = torch.clamp(rewards, min=0)
+                policy_loss = self.calculate_policy_loss(thought_ids, rewards)
+                loss = loss + policy_loss
+        else:
+            loss = None
+
+        if not return_dict:
+            output = (mixed_logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss if loss is not None else None,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        log_dict = self.log_dict if self.training else self.eval_log_dict
+
+        if self.training and self.kill_after is not None and self.training_steps // self.gradient_accumulation_steps > self.kill_after:
+            raise ValueError("Killed after")
+
+        if not self.training:
+            n_ahead_talk_to_restore = self.n_ahead_talk
+            n_passes_to_restore = self.n_passes
+            self.n_ahead_talk = 1
+            self.n_passes = 1
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        assert self.cumulative_residual or self.clever_residual or self.skip_residual or self.no_residual
+        assert not (self.skip_residual and self.use_policy_loss)
+
+        if self.tokenized_thought_prefix is None and self.use_thought_prefix:
+            self.tokenized_thought_prefix = self.tokenizer(self.thought_prefix, return_tensors="pt", add_special_tokens=False)["input_ids"]
+
+        def apply_head(head, states, detach=False):
+            if detach:
+                head_weight = head.weight.detach()
+            else:
+                head_weight = head.weight
+            head_weight = head_weight.to(states.device)
+            return (head_weight @ states.transpose(-1, -2)).transpose(-1, -2).contiguous()
+    
+        def idx_if_sequential(head, idx=0):
+            if isinstance(head, nn.Sequential) or isinstance(head, nn.ModuleList):
+                return idx_if_sequential(head[idx], idx=idx)
+            return head
+
+        def none_repeat_interleave(x, n):
+            if x is None:
+                return x
+            return x.repeat_interleave(n, dim=0)
+
+        if self.n_passes > 1:
+            input_ids = none_repeat_interleave(input_ids, self.n_passes)
+            attention_mask = none_repeat_interleave(attention_mask, self.n_passes)
+            position_ids = none_repeat_interleave(position_ids, self.n_passes)
+            inputs_embeds = none_repeat_interleave(inputs_embeds, self.n_passes)
+            labels = none_repeat_interleave(labels, self.n_passes)
+            if past_key_values is not None:
+                past_key_values = [none_repeat_interleave(p, self.n_passes) for p in past_key_values]
+        cur_token_indices = torch.arange(input_ids.shape[1], device=input_ids.device)
+
+        self.tokenizer_has_start_thought_token = True
+        self.tokenizer_has_end_thought_token = True
+        if self.start_token_id is None:
+            self.start_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+            if self.start_token_id == 0:
+                self.start_token_id = self.tokenizer.bos_token_id
+                self.tokenizer_has_start_thought_token = False
+            elif self.use_start_thought_token:
+                # base_start_id = self.tokenizer.convert_tokens_to_ids(self.initial_start_token)
+                base_start_id = self.tokenizer.encode(self.initial_start_token, add_special_tokens=False)[0]
+                if self.initialize_thought_embedding_to_normal:
+                    self.start_embedding.data = torch.zeros_like(self.start_embedding.data)
+                else:
+                    self.start_embedding.data[0] = self.model.embed_tokens.weight.data[base_start_id].clone().detach() / self.embedding_scale
+                self.start_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
+        if self.end_token_id is None:
+            self.end_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+            if self.end_token_id == 0:
+                self.end_token_id = self.tokenizer.eos_token_id
+                self.tokenizer_has_end_thought_token = False
+            elif self.use_end_thought_token:
+                # base_end_id = self.tokenizer.convert_tokens_to_ids(self.initial_end_token)
+                base_end_id = self.tokenizer.encode(self.initial_end_token, add_special_tokens=False)[0]
+                if self.initialize_thought_embedding_to_normal:
+                    self.end_embedding.data = torch.zeros_like(self.end_embedding.data)
+                else:
+                    self.end_embedding.data[0] = self.model.embed_tokens.weight.data[base_end_id].clone().detach() / self.embedding_scale
+                self.end_embedding.data[1] = torch.log(self.model.embed_tokens.weight.data.std(dim=0) * self.thought_init_std_scale / self.embedding_scale)
+
+        if not self.rm_initialized and (self.n_ahead > 1 or not self.base_original_mode):
+            self.rm_initialized = True                        
+            if not self.use_shallow_talk:
+                head = self.talk_head[0]
+                cur_head = head[-1] if isinstance(head, nn.Sequential) else head
+                talk_input_dim = cur_head.weight.data.shape[1]
+                talk_output_dim = 1 if self.use_weighted_talk_head else self.lm_head.weight.data.shape[0]
+                cur_head.weight.data = torch.zeros(talk_output_dim, talk_input_dim, device=cur_head.weight.device, dtype=cur_head.weight.dtype)
+            else:
+                # convert to identity transform
+                def lambda_transform(cur_head):
+                    if cur_head.weight.data.shape[0] != cur_head.weight.data.shape[1]:
+                        return torch.cat([
+                        torch.eye(
+                            cur_head.weight.data.shape[0],
+                            device=cur_head.weight.device,
+                            dtype=cur_head.weight.dtype
+                        ),
+                        torch.zeros(
+                            cur_head.weight.data.shape[0],
+                            cur_head.weight.data.shape[1] - cur_head.weight.data.shape[0],
+                            device=cur_head.weight.device,
+                            dtype=cur_head.weight.dtype
+                        )], dim=1)
+                    return torch.eye(
+                        cur_head.weight.data.shape[0],
+                        device=cur_head.weight.device,
+                        dtype=cur_head.weight.dtype
+                    )
+                if isinstance(self.talk_head[0], nn.Sequential):
+                    for cur_head in self.talk_head[0]:
+                        # if it has weights
+                        if hasattr(cur_head, "weight"):
+                            cur_head.weight.data = lambda_transform(cur_head)
+                else:
+                    self.talk_head[-1].weight.data = lambda_transform(self.talk_head[0])
+
+        loss = None
+        prev_rm_tokens = None
+        cur_rm_tokens = None
+        prev_rm_logits = None
+        prev_sample_probs = None
+        did_skip_sampling = None
+        skip_sampling = None
+        sample_probs = None
+        hidden_states = None
+        logits = None
+        talk_kl_penalty = None
+        rm_logits = None
+        residual_logits = None
+        probabilities_2d = None
+        prev_probabilities_2d = None
+        policy_reward = None
+        logits_to_output = None
+        batch_size, seq_len = input_ids.shape
+        base_input_ids = input_ids.clone()
+        loss_list = []
+        dqn_loss_list = []
+        sampled_token_history = []
+        sample_probs_history = []
+        action_loglikelihoods_list = []
+
+        if self.use_end_thought_token or self.use_start_thought_token:
+            if not self.use_reparam_for_thought_embeddings:
+                start_embedding = self.start_embedding[0].unsqueeze(0) * self.embedding_scale
+                end_embedding = self.end_embedding[0].unsqueeze(0) * self.embedding_scale
+            else:
+                start_embedding = self.start_embedding * self.embedding_scale
+                end_embedding = self.end_embedding * self.embedding_scale
+            base_embeddings = self.model.embed_tokens.weight
+            if self.train_only_thinking_embedding:
+                base_embeddings = base_embeddings.detach()
+        # # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        fwd_iters = 1 if self.original_mode else self.n_ahead + self.n_ahead_talk - 1
+        for ahead_idx in range(fwd_iters):
+            past_key_values_length = 0
+            if past_key_values is not None:
+                use_legacy_cache = not isinstance(past_key_values, Cache)
+                if use_legacy_cache:
+                    past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                past_key_values_length = past_key_values.get_usable_length(seq_len)
+
+            if position_ids is None:
+                device = input_ids.device if input_ids is not None else inputs_embeds.device
+                position_ids = torch.arange(
+                    past_key_values_length, seq_len + past_key_values_length, dtype=torch.long, device=device
+                )
+                position_ids = position_ids.unsqueeze(0).view(-1, seq_len)
+            else:
+                position_ids = position_ids.view(-1, seq_len).long()
+
+            if inputs_embeds is None:
+                contains_start = self.use_start_thought_token and (input_ids == self.start_token_id).any()
+                contains_end = self.use_end_thought_token and (input_ids == self.end_token_id).any()
+                contains_thought = contains_start or contains_end
+                if contains_thought:
+                    thought_id = self.start_token_id if contains_start else self.end_token_id
+                    cur_thought_embedding = start_embedding if contains_start else end_embedding
+                    if self.use_reparam_for_thought_embeddings:
+                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
+                        inputs_embeds = inputs_embeds.detach() * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
+                        if contains_start:
+                            sampled_start = inputs_embeds.clone().detach()
+                        if contains_end:
+                            sampled_end = inputs_embeds.clone().detach()
+                    else:
+                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
+                else:
+                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
+                        inputs_embeds = self.model.embed_tokens(input_ids)
+            
+            if self.n_ahead != 1 or self.n_ahead_talk != 1 or self.comparison_mode:
+                if attention_mask is None:
+                    base_attention_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=0).to(input_ids.device)
+                    base_attention_mask = base_attention_mask.view(1, 1, seq_len, seq_len)
+                    base_attention_mask = base_attention_mask.repeat(input_ids.shape[0], 1, 1, 1)
+                    attention_mask = base_attention_mask
+                    breakpoint()
+                elif attention_mask.dim() == 2:
+                    if seq_len + past_key_values_length != attention_mask.shape[-1]:
+                        breakpoint()
+                        attention_mask = torch.cat(
+                            [torch.ones((attention_mask.shape[0], past_key_values_length), dtype=attention_mask.dtype, device=attention_mask.device), attention_mask],
+                            dim=-1
+                        )
+                    # # if the attention mask 
+                    attention_mask = _prepare_4d_causal_attention_mask(
+                        attention_mask,
+                        (batch_size, seq_len),
+                        inputs_embeds,
+                        past_key_values_length,
+                        sliding_window=self.config.sliding_window,
+                    )
+
+            outputs = self.model(
+                # input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            prev_hidden_states = hidden_states
+            hidden_states = outputs[0]
+            prev_rm_logits = rm_logits  # for policy gradient
+            prev_rm_tokens = cur_rm_tokens  # for policy gradient
+
+            if ahead_idx == 0:
+                hidden_states_lm = hidden_states
+                logits = self.lm_head(hidden_states_lm)
+                base_hidden_states = hidden_states.clone()
+                initial_loss_logits = logits.clone()
+                if self.optimize_lm_head_only_at_start or self.optimize_model_only_at_start:
+                    logits = logits.detach()
+                    base_hidden_states = base_hidden_states.detach()
+                if self.optimize_model_only_at_start:
+                    hidden_states = hidden_states.detach()
+                base_logits = logits.clone()
+            else:
+                talk_hidden_states = hidden_states
+                if self.merged_lm_and_talk_heads:
+                    assert self.no_residual
+                    residual_logits = self.lm_head(hidden_states)
+                    talk_hidden_states = hidden_states
+                else:
+                    if ahead_idx > self.n_ahead - 1:
+                        cur_base_hidden = torch.cat([
+                            base_hidden_states[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_hidden_states[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                    else:
+                        cur_base_hidden = base_hidden_states
+
+                    if self.use_concat_talk_head:
+                        # concatenate the hidden states with the original hidden states
+                        head_input_hidden_states = torch.cat([cur_base_hidden, talk_hidden_states], dim=-1)
+                    else:
+                        head_input_hidden_states = talk_hidden_states
+
+                    residual_logits = self.talk_head[0](head_input_hidden_states)
+                    if self.use_shallow_talk:
+                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)                        
+                    residual_logits = residual_logits.to(logits.device)
+                    if self.use_weighted_talk_head:
+                        # combine the cur_base_hidden with the talk_hidden_states according to the weighted head
+                        residual_logits = cur_base_hidden * (1 - residual_logits) + talk_hidden_states * residual_logits
+                        residual_logits = apply_head(self.lm_head, residual_logits, detach=self.optimize_lm_head_only_at_start)
+
+                assert sum([self.cumulative_residual, self.clever_residual, self.skip_residual, self.no_residual]) == 1
+                if self.clever_residual:
+                    if ahead_idx >= self.n_ahead - 1:
+                        # get the logits shifted according to the current talk ahead
+                        cur_base_logits = torch.cat([
+                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                        if self.optimize_lm_head_only_at_start:
+                            cur_base_logits = cur_base_logits.detach()
+                        logits = cur_base_logits + residual_logits
+                    else:
+                        logits += residual_logits / self.n_ahead
+                elif self.cumulative_residual:
+                    if self.residual_talk_head:
+                        if ahead_idx < self.n_ahead:
+                            logits += residual_logits
+                        else:
+                            # get the logits shifted according to the current talk ahead
+                            cur_base_logits = torch.cat([
+                                base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                                base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                            ], dim=-2)
+                            if self.optimize_lm_head_only_at_start:
+                                cur_base_logits = cur_base_logits.detach()
+                            logits = cur_base_logits + residual_logits
+                    else:
+                        if ahead_idx < self.n_ahead:
+                            logits += residual_logits
+                        else:
+                            logits = residual_logits
+                elif self.skip_residual:
+                    if ahead_idx >= self.n_ahead:
+                        # get the logits shifted according to the current talk ahead
+                        cur_base_logits = torch.cat([
+                            base_logits[..., ahead_idx - self.n_ahead + 1:, :],
+                            base_logits[..., :ahead_idx - self.n_ahead + 1, :]
+                        ], dim=-2)
+                        if self.optimize_lm_head_only_at_start:
+                            cur_base_logits = cur_base_logits.detach()
+                        logits = cur_base_logits
+                elif self.no_residual:
+                    logits = residual_logits
+                else:
+                    logits = base_logits + residual_logits
+
+            attempted = False
+            talk_loss_list = []
+            if self.original_mode or (self.n_ahead == 1) or (self.comparison_mode and ahead_idx == 0):# or (self.optimize_lm_head_only_at_start and ahead_idx == 0):
+                loss = None
+                attempted = True
+
+                if labels is not None:
+                    for shift_amount in range(self.n_ahead_talk):
+                        # Shift so that tokens < n predict n
+                        #  ab[cde]f
+                        # abc[def]
+                        if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
+                            loss_logits = initial_loss_logits
+                        else:
+                            loss_logits = logits
+                        shift_logits = loss_logits[..., shift_amount:-1, :].contiguous()
+                        shift_labels = labels[..., 1 + shift_amount:].contiguous()
+                        # Flatten the tokens
+                        loss_fct = CrossEntropyLoss(reduction="none")
+                        shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                        shift_labels = shift_labels.view(-1).clone()
+                        # Enable model parallelism
+                        shift_labels[shift_labels == self.tokenizer.pad_token_id] = -100
+                        shift_labels = shift_labels.to(shift_logits.device)
+                        loss = loss_fct(shift_logits, shift_labels)
+                        if not self.comparison_mode and not (self.optimize_lm_head_only_at_start and (self.n_ahead + self.n_ahead_talk > 2)) or self.original_mode:
+                            loss_list.append(loss)
+                        talk_loss_list.append(nonzero_mean(loss).detach())
+            
+            if not attempted or self.comparison_mode:
+                rm_hidden_states = hidden_states
+                # print("Magnitude of RM hidden states before RM head", rm_hidden_states.norm())
+                rm_logits = apply_head(self.lm_head, rm_hidden_states, detach=self.optimize_lm_head_only_at_start)
+                    
+                # don't allow it to predict the thinking token
+                if self.tokenizer_has_start_thought_token:                    
+                    rm_logits[..., self.start_token_id] = -1e10
+                if self.tokenizer_has_end_thought_token:
+                    rm_logits[..., self.end_token_id] = -1e10
+                probabilities = rm_logits
+                if probabilities_2d is not None:
+                    prev_probabilities_2d = probabilities_2d.clone()
+                probabilities_2d = probabilities.view(-1, probabilities.size(-1))
+
+                did_skip_sampling = skip_sampling
+                skip_sampling = False
+                if ahead_idx == 0 and self.use_start_thought_token:
+                    override_token = self.start_token_id
+                elif self.use_thought_prefix and ahead_idx < self.tokenized_thought_prefix.shape[-1]:
+                    override_token = self.tokenized_thought_prefix[..., ahead_idx]
+                elif ahead_idx == self.n_ahead - 2 and self.use_end_thought_token:
+                    override_token = self.end_token_id
+                else:
+                    override_token = None
+                if override_token is not None and self.n_ahead > 1:
+                    # always start with the start token
+                    probabilities_2d = torch.zeros_like(probabilities_2d)
+                    probabilities_2d[:, override_token] = 1.0
+                    skip_sampling = True
+                elif ahead_idx >= self.n_ahead - 1:
+                    if labels is not None:  # we're in the talk phase
+                        cur_talk_n = ahead_idx - (self.n_ahead - 1) + 1
+                        # print("Setting rm to labels", cur_talk_n, "during", ahead_idx)
+                        shift_labels = labels[..., cur_talk_n:].contiguous().to(probabilities_2d.device)
+                        padding = torch.full_like(
+                            labels[..., :cur_talk_n],
+                            self.tokenizer.pad_token_id,
+                            dtype=torch.long,
+                            device=shift_labels.device
+                        )
+                        new_rm_tokens = torch.cat(
+                            [shift_labels, padding],
+                            dim=-1
+                        )
+                        # convert rm tokens to one-hot
+                        probabilities_2d = F.one_hot(new_rm_tokens, num_classes=self.vocab_size).reshape(-1, self.vocab_size).to(probabilities_2d.dtype)
+                        skip_sampling = True
+                    else:
+                        continue
+                temperature = self.gumbel_temperature if self.training else 0.001
+                prev_sample_probs = sample_probs
+                sample_probs = probabilities_2d
+                if ahead_idx < self.n_ahead - 1 and not skip_sampling:
+                    probabilities_2d = F.gumbel_softmax(sample_probs, tau=temperature, hard=True, dim=-1)
+                    if self.gumbel_detach:
+                        probabilities_2d = probabilities_2d.detach()
+                sampled_token_history.append(probabilities_2d.argmax(dim=-1).detach().cpu())
+                # convert rm logits directly to embeddings
+                contains_start = self.use_start_thought_token and (probabilities_2d[..., self.start_token_id].sum() > 0)
+                contains_end = self.use_end_thought_token and (probabilities_2d[..., self.end_token_id].sum() > 0)
+                contains_thought = contains_start or contains_end
+
+                if not contains_thought:
+                    with torch.set_grad_enabled(not self.train_only_thinking_embedding):
+                        inputs_embeds = probabilities_2d @ (self.model.embed_tokens.weight.to(probabilities.device).to(probabilities.dtype))
+                else:
+                    thought_id = self.start_token_id if contains_start else self.end_token_id
+                    cur_thought_embedding = start_embedding if contains_start else end_embedding
+                    if self.use_reparam_for_thought_embeddings:
+                        inputs_embeds = torch.randn(batch_size, seq_len, self.model.config.hidden_size, device=input_ids.device, dtype=cur_thought_embedding.dtype)
+                        inputs_embeds = inputs_embeds * torch.exp(cur_thought_embedding[1]) + cur_thought_embedding[0]
+                        if contains_start:
+                            sampled_start = inputs_embeds.clone().detach()
+                        else:
+                            sampled_end = inputs_embeds.clone().detach()
+                    else:
+                        inputs_embeds = cur_thought_embedding.unsqueeze(0).repeat(batch_size, seq_len, 1)
+                        inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
+                inputs_embeds = inputs_embeds.view(probabilities.size(0), probabilities.size(1), -1).to(self.model.embed_tokens.weight.dtype)
+
+                if len(attention_mask.shape) == 2:
+                    breakpoint()
+                else:
+                    original_attention = attention_mask[..., :attention_mask.shape[-2]]
+                    if self.use_upper_triangular:
+                        new_attention = original_attention
+                    else:
+                        original_attention = original_attention == attention_mask.max()
+                        # because eye isn't implemented for BF16, we need to handle the case
+                        if not attention_mask.dtype == torch.bfloat16:
+                            new_attention = torch.eye(
+                                seq_len, dtype=attention_mask.dtype, device=attention_mask.device
+                            )
+                        else:
+                            new_attention = torch.eye(
+                                seq_len, dtype=torch.float32, device=attention_mask.device
+                            ).to(attention_mask.dtype)
+
+                        new_attention = new_attention.view(1, 1, seq_len, seq_len).repeat(input_ids.shape[0], 1, 1, 1)
+                        new_attention = new_attention * original_attention
+                        new_attention[new_attention == 0] = attention_mask.min()
+                        new_attention[new_attention == 1] = attention_mask.max()
+                    attention_mask = torch.cat([attention_mask, new_attention], dim=-1)
+                past_key_values = outputs.past_key_values
+                position_ids = position_ids + 1
+
+                if labels is not None and (self.n_ahead > 1 or not self.base_original_mode):
+                    # Shift so that tokens < n predict n
+                    # logits: abcdef -> bcdef? -> cdef??
+                    # labels: abcdef -> ?bcdef -> ??cdef
+                    if ahead_idx == 0 and self.optimize_lm_head_only_at_start:
+                        loss_logits = initial_loss_logits
+                    else:
+                        loss_logits = logits
+                    shift_idx = 1 + max(0, ahead_idx - (self.n_ahead - 1))
+                    shift_logits = loss_logits[..., :-shift_idx, :].contiguous()
+                    shift_labels = labels[..., shift_idx:].contiguous()
+                    # Flatten the tokens
+                    loss_fct = CrossEntropyLoss(reduction="none")
+                    shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                    shift_labels = shift_labels.view(-1)
+                    # Enable model parallelism
+                    shift_labels = shift_labels.to(shift_logits.device)
+                    # if shift_labels.min() == self.tokenizer.pad_token_id:
+                    shift_labels = torch.where(shift_labels == self.tokenizer.pad_token_id, -100, shift_labels)
+                    unreduced_loss = loss_fct(shift_logits, shift_labels)
+                    if torch.any(unreduced_loss != unreduced_loss):
+                        raise ValueError("NaN loss")
+                    unreduced_loss = unreduced_loss.reshape(logits.shape[0], -1)
+                    loss_list.append(unreduced_loss)
+
+
+                    if self.use_policy_loss and ahead_idx > 0 and (ahead_idx > 1 or not self.use_start_thought_token):
+                        # we treat the change in loss as the reward
+                        previous_loss = loss_list[-2]
+                        # for example, suppose n_ahead = 3 and n_ahead_talk = 2
+                        # note that we end at self.n_ahead + self.n_ahead_talk - 2
+                        # in this case, 5 - 2 = 3, so we end at ahead_idx = 3
+                        # we also predict the next token at ahead_idx = 2
+                        # when we get to ahead_idx = 2, we predict ahead
+                        # so we shift by 1
+                        # note that this is ahead_idx = n_ahead - 1
+                        # when we get to ahead_idx = 3, we predict ahead
+                        # so we shift by 2
+                        # note that this is ahead_idx = n_ahead
+                        if ahead_idx < self.n_ahead - 1:
+                            shift_amount = 0
+                            original_dqn_reward = (previous_loss - unreduced_loss).detach()
+                            if self.first_and_last_mode:
+                                original_dqn_reward = original_dqn_reward * 0.0
+                        else:
+                            # logits vs cur_policy_shift_logits
+                            # let's look at rm_logits and prev_rm_logits
+                            shift_amount = max(0, ahead_idx - (self.n_ahead - 1))
+                            # let's say shift_amount = 2
+                            # abcdefg -> bcdefg? -> cdefg??
+                            # logits = [a b]c d e f[g]
+                            # labels = [a b c]d e f g
+                            cur_policy_shift_logits = initial_loss_logits[..., shift_amount:-1, :].contiguous().detach()
+                            cur_policy_shift_labels = labels[..., 1 + shift_amount:].contiguous()
+                            # Flatten the tokens
+                            cur_policy_loss_fct = CrossEntropyLoss(reduction="none")
+                            cur_policy_shift_logits = cur_policy_shift_logits.view(-1, self.config.vocab_size)
+                            cur_policy_shift_labels = cur_policy_shift_labels.view(-1).clone()
+                            # Enable model parallelism
+                            cur_policy_shift_labels[cur_policy_shift_labels == self.tokenizer.pad_token_id] = -100
+                            cur_policy_shift_labels = cur_policy_shift_labels.to(cur_policy_shift_labels.device)
+                            cur_policy_reward_base_loss = loss_fct(
+                                cur_policy_shift_logits, cur_policy_shift_labels.to(cur_policy_shift_logits.device)
+                            ).reshape(logits.shape[0], -1)
+                            original_dqn_reward = cur_policy_reward_base_loss.detach() - unreduced_loss
+                                
+                        if not did_skip_sampling:
+                            nonzero_indices = prev_probabilities_2d.nonzero()
+                            action_loglikelihoods = F.log_softmax(prev_sample_probs / self.reinforce_temperature, dim=-1)[nonzero_indices[:, 0], nonzero_indices[:, 1]]
+                            action_loglikelihoods_2d = action_loglikelihoods.reshape(batch_size, -1)[:, :-1 - shift_amount]
+                            action_loglikelihoods_list.append(action_loglikelihoods_2d)
+                        if policy_reward is None:
+                            policy_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
+                        else:
+                            if self.n_ahead_talk > shift_amount:
+                                added_reward = original_dqn_reward[:, :-(self.n_ahead_talk - shift_amount)]
+                            else:
+                                added_reward = original_dqn_reward
+                            policy_reward += added_reward
+                    
+                    if self.use_policy_loss and ahead_idx == self.n_ahead + self.n_ahead_talk - 2:
+                        # only compute during the thinking phase
+                        if self.use_reparam_for_thought_embeddings and (self.use_start_thought_token or self.use_end_thought_token):
+                            # sampled_start, sampled_end
+                            # calculate the log likelihood of the start and end embeddings sampled from a multivariate normal distribution
+                            # with mean start_embedding[0] and standard deviation start_embedding[1]
+                            if self.use_start_thought_token:
+                                exp_start_std = torch.exp(start_embedding[1])
+                                start_loglikelihood = -0.5 * (sampled_start.detach() - start_embedding[0]) ** 2 / exp_start_std ** 2 - start_embedding[1] - 0.5 * math.log(2 * math.pi)
+                                start_loglikelihood = start_loglikelihood.mean(dim=-1)
+                            if self.use_end_thought_token:
+                                exp_end_std = torch.exp(end_embedding[1])
+                                end_loglikelihood = -0.5 * (sampled_end.detach() - end_embedding[0]) ** 2 / exp_end_std ** 2 - end_embedding[1] - 0.5 * math.log(2 * math.pi)
+                                end_loglikelihood = end_loglikelihood.mean(dim=-1)
+                            # we use the mean instead of the sum to prevent dependence on the dimensionality of the embeddings
+                            if self.use_end_thought_token and self.use_policy_loss_for_end_thought:
+                                action_loglikelihoods_list.append(end_loglikelihood)
+                            if self.use_start_thought_token:
+                                action_loglikelihoods_list.append(start_loglikelihood)                                
+
+                        if ahead_idx == self.n_ahead + self.n_ahead_talk - 2 and self.eval_mode:
+                            with torch.no_grad():
+                                # calculate the 0.75 quantile of the rewards
+                                filtered_tokens = input_ids[:, :policy_reward.shape[-1]].cpu().detach().numpy().flatten()
+                                filtered_tokens_mask = filtered_tokens != self.tokenizer.pad_token_id
+                                filtered_tokens = filtered_tokens[filtered_tokens_mask]
+                                filtered_rewards = policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten()
+                                filtered_rewards = filtered_rewards[filtered_tokens_mask]
+
+                                abs_reward_list = np.abs(policy_reward.float().cpu().detach().numpy()[:, :seq_len - self.n_ahead_talk].flatten())
+                                abs_reward_list = abs_reward_list[filtered_tokens_mask]
+                                medium_quantile = np.quantile(abs_reward_list, 0.5)
+                                upper_quantile = np.quantile(abs_reward_list, 0.95)
+
+                        for action_loglikelihoods_2d in action_loglikelihoods_list:
+                            train_policy_reward = policy_reward
+
+                            # discard rewards below the mean
+                            if self.trice_mode and self.n_passes > 1:
+                                batched_policy_reward = train_policy_reward.reshape(-1, self.n_passes, train_policy_reward.shape[-1])
+                                # average over the passes
+                                train_policy_reward = batched_policy_reward - batched_policy_reward.mean(dim=1, keepdim=True)
+                                train_policy_reward = train_policy_reward.reshape(-1, train_policy_reward.shape[-1])
+                                
+                            if self.subtract_mean_reward:
+                                train_policy_reward = train_policy_reward - train_policy_reward.mean()
+                            if self.remove_negative_rewards:
+                                fixed_policy_reward = train_policy_reward.detach().clamp(min=0)
+                            else:
+                                fixed_policy_reward = train_policy_reward.detach()
+                            actor_loss = -fixed_policy_reward * action_loglikelihoods_2d[:, :policy_reward.shape[-1]].to(policy_reward.device)
+                            if action_loglikelihoods_2d.mean() < -1e4 and not self.use_policy_loss_just_for_thoughts:
+                                # This will only happen when we force the next token to be the end of thought token
+                                break
+                            dqn_loss_list.append(actor_loss.mean())
+
+        if loss_list:
+            if self.first_and_last_mode:
+                loss = sum(
+                    self.loss_mean(loss_list[-(i + 1)]) for i in range(self.n_ahead_talk)
+                ) * (1 - self.original_loss_weight) / self.n_ahead_talk
+                loss = loss + self.loss_mean(loss_list[0]) * self.original_loss_weight
+                # Let's NaN out the others
+                # e.g. if n_ahead_talk = 2 and the list is 5 long, we want to NaN out 1, 2 but keep 0, 3, 4
+                for i in range(1, len(loss_list) - self.n_ahead_talk):
+                    loss_list[i] = loss_list[i] * math.nan
+            elif self.first_only:
+                loss = self.loss_mean(loss_list[0])
+            elif self.final_only_mode:
+                loss = sum(
+                    self.loss_mean(loss_list[-i]) for i in range(1, self.n_ahead_talk + 1)
+                ) / self.n_ahead_talk   
+            else:
+                loss = None
+                for i in range(len(loss_list)):
+                    cur_loss = self.loss_mean(loss_list[i])
+                    if loss is not None:
+                        loss = loss + cur_loss.to(loss.device)
+                    else:
+                        loss = cur_loss
+                loss = loss / len(loss_list)
+            
+            loss = loss * self.base_loss_beta
+
+        if dqn_loss_list:
+            dqn_loss = sum(dqn_loss_list) / len(dqn_loss_list)
+            if self.include_policy_loss:
+                if loss is not None:
+                    loss += dqn_loss * self.policy_loss_beta
+                else:
+                    loss = dqn_loss * self.policy_loss_beta
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+    
+        base_log_dict = {
+            f"loss_{i}": nonzero_mean(loss_list[i]) for i in range(len(loss_list))
+        }
+
+        if loss is not None:
+            base_log_dict["loss_train"] = loss.item()
+        
+        for loss_key, loss_val in base_log_dict.items():
+            log_dict[loss_key] += loss_val / self.n_tokens_print
+                
+        if self.use_policy_loss and policy_reward is not None:
+            log_dict["policy_loss"] += dqn_loss / self.n_tokens_print
+            log_dict["policy_reward"] += policy_reward.mean() / self.n_tokens_print
+
+        if not loss_list:
+            if loss is not None:
+                log_dict["loss_0"] += loss / self.n_tokens_print
+        else:
+            log_dict["loss_final"] += nonzero_mean(loss_list[-1]) / self.n_tokens_print
+            log_dict["loss_talk"] += sum(nonzero_mean(cur_loss_item) for cur_loss_item in loss_list[-self.n_ahead_talk:]) / self.n_ahead_talk / self.n_tokens_print
+
+        # also log relative losses to loss_0
+        if loss_list:
+            for i in range(len(loss_list)):
+                talk_idx = min(max(i - (self.n_ahead - 1), 0), len(talk_loss_list) - 1)
+                if not talk_loss_list:
+                    cur_talk_loss = nonzero_mean(loss_list[0])
+                else:
+                    cur_talk_loss = talk_loss_list[talk_idx]
+                log_dict[f"rel_loss_{i}"] += (nonzero_mean(loss_list[i]) - cur_talk_loss) / self.n_tokens_print
+        if self.training:
+            self.training_steps += 1
+
+        if not self.training:
+            self.n_ahead_talk = n_ahead_talk_to_restore
+            self.n_passes = n_passes_to_restore
+        return CausalLMOutputWithPast(
+            loss=loss if loss is not None else None,
+            logits=(rm_logits if self.n_ahead > 1 else logits) if not self.output_logits_at_the_end else logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+class MistralQuietForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MistralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.max_thoughts = config.max_thoughts
+        self.merged_lm_and_talk_heads = config.merged_lm_and_talk_heads
+        self.use_concat_talk_head = config.use_concat_talk_head
+        self.use_shallow_talk = config.use_shallow_talk
+        self.use_complex_talk_head = config.use_complex_talk_head
+        self.use_weighted_talk_head = config.use_weighted_talk_head
+        # the weighted head will output a single value, so it can't be passed to the lm head
+        assert not (self.use_weighted_talk_head and self.use_shallow_talk)
+
+        self.n_ahead = 1
+        self.n_ahead_talk = 1
+        self.n_passes = 1
+        self.n_tokens_print = 1
+        self.gradient_accumulation_steps = 1
+        self.training_steps = 0
+        self.tokenizer = None
+        self.start_token_id = None
+        self.end_token_id = None
+        self.rm_initialized = False
+        self.residual_talk_head = True
+        self.thought_init_std_scale = 1e-2
+
+        self.final_only_mode = False
+        self.first_and_last_mode = True
+        self.first_only = False
+        self.original_loss_weight = 0.5
+
+        self.cumulative_residual = False
+        self.clever_residual = False
+        self.skip_residual = False
+        self.no_residual = True
+
+        self.optimize_lm_head_only_at_start = False
+        self.optimize_model_only_at_start = False
+
+        if self.optimize_model_only_at_start:
+            raise NotImplementedError
+        self.train_only_thinking_embedding = False
+        self.weighted_embeddings = False
+        self.use_start_thought_token = True
+        self.use_end_thought_token = True
+        self.initialize_thought_embedding_to_normal = False
+        self.initial_start_token = "---"
+        self.initial_end_token = "---"
+        self.output_logits_at_the_end = True
+
+        self.gumbel_temperature = 0.001
+
+        self.use_policy_loss = True
+        self.include_policy_loss = True
+        self.trice_mode = True
+        self.remove_negative_rewards = True
+        self.use_policy_loss_for_end_thought = True
+        
+        self.base_original_mode = False
+        self.original_mode = False
+
+        self.thought_prefix = "(Let's think step by step"
+        self.tokenized_thought_prefix = None
+        self.log_dict = defaultdict(int)
+        self.eval_log_dict = defaultdict(int)
+        self.print_final_only = True
+        self.loss_mean = loss_mean
+        self.all_rewards = []
+        self.all_unreduced_losses = []
+        self.kill_after = 100
+
+        self.start_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+        self.end_embedding = nn.Parameter(torch.zeros(2, self.model.config.hidden_size))
+
+        self.policy_loss_beta = 1e6
+        self.embedding_scale = 1e2
+        self.reinforce_temperature = 3
+        self.base_loss_beta = 1
+
+        # Not used in the paper:
+        self.use_thought_prefix = False
+        self.use_reparam_for_thought_embeddings = False
+        self.use_upper_triangular = False
+        self.subtract_mean_reward = False
+        self.comparison_mode = False
+        self.gumbel_detach = True
+    
+        # For visualization
+        self.eval_mode = False
+
+        num_talk = 1
+        talk_input_dim = config.hidden_size if not self.use_concat_talk_head else config.hidden_size * 2
+        if self.use_weighted_talk_head:
+            talk_output_dim = 1
+        else:
+            talk_output_dim = config.hidden_size if self.use_shallow_talk else config.vocab_size
+
+        if not self.merged_lm_and_talk_heads:
+            if self.use_complex_talk_head:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, config.hidden_size),
+                    nn.ReLU(),
+                    nn.Linear(config.hidden_size, talk_output_dim, bias=False)
+                )])
+            else:
+                self.talk_head = nn.ModuleList([nn.Sequential(
+                    nn.Linear(talk_input_dim, talk_output_dim, bias=False)
+                )])
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+    def calculate_policy_loss(self, thoughts, rewards):
+        thought_log_probs = []
+        for thought in thoughts:
+            thought_log_prob = self.lm_head(thought).log_softmax(dim=-1)
+            thought_log_probs.append(thought_log_prob)
+        
+        thought_log_probs = torch.stack(thought_log_probs, dim=1)  # (batch_size, num_thoughts, seq_length, vocab_size)
+        thought_probs = torch.exp(thought_log_probs)
+        
+        policy_loss = -torch.mean(thought_log_probs * rewards.unsqueeze(-1).unsqueeze(-1))
+        
+        return policy_loss
+    
+    def _generate_thoughts(self, hidden_states, max_length):
+        batch_size = hidden_states.size(0)
+        thought_ids = torch.zeros((batch_size, self.config.max_thoughts, max_length), dtype=torch.long, device=hidden_states.device)
+        thought_embeddings = []
+
+        for i in range(self.config.max_thoughts):
+            thought_input_ids = torch.zeros((batch_size, 1), dtype=torch.long, device=hidden_states.device)
+            thought_outputs = self.generate(
+                input_ids=thought_input_ids,
+                max_length=max_length,
+                do_sample=True,
+                top_k=50,
+                top_p=0.95,
+                pad_token_id=self.config.pad_token_id,
+                eos_token_id=self.config.eos_token_id,
+            )
+            thought_ids[:, i, :] = thought_outputs
+            thought_embeddings.append(self.get_input_embeddings()(thought_outputs))
+
+        thought_embeddings = torch.stack(thought_embeddings, dim=1)
+        return thought_ids, thought_embeddings
+
+    @torch.no_grad()
+    def infer(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        batch_size, seq_len = input_ids.shape
+
+        # Save the original input_ids and attention_mask for later use
+        original_input_ids = input_ids.clone()
+        original_attention_mask = attention_mask.clone() if attention_mask is not None else None
+
+        # Append the start thought token to the input sequence
+        start_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|startthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[start_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Generate the continuation
+        continuation_length = self.n_ahead - 2
+        new_key_values = past_key_values
+        
+        start_time = time.time()
+        for continuation_idx in range(continuation_length):
+            outputs = self.model(
+                input_ids=input_ids if continuation_idx == 0 else next_token_id.unsqueeze(-1).to(input_ids.device),
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=new_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            new_key_values = outputs.past_key_values
+
+            hidden_states = outputs[0]
+
+            logits = self.lm_head(hidden_states)
+            logits = logits[:, -1, :]  # Only consider the last token
+
+            # Apply Gumbel-Softmax to the logits
+            next_token_logits = F.gumbel_softmax(logits, tau=self.gumbel_temperature, hard=True, dim=-1)
+            next_token_id = torch.argmax(next_token_logits, dim=-1)
+
+            # Append the generated token to the input sequence
+            input_ids = torch.cat([input_ids, next_token_id.unsqueeze(-1).to(input_ids.device)], dim=-1)
+            seq_len += 1
+
+            # Update the attention mask
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Append the end thought token to the input sequence
+        end_thought_token_id = self.tokenizer.convert_tokens_to_ids("<|endthought|>")
+        input_ids = torch.cat([input_ids, torch.tensor([[end_thought_token_id]] * batch_size).to(input_ids.device)], dim=-1)
+        seq_len += 1
+
+        # Update the attention mask
+        if attention_mask is not None:
+            attention_mask = torch.cat([attention_mask, torch.ones((batch_size, 1)).to(attention_mask.device)], dim=-1)
+
+        # Get the hidden states before and after the thought
+        outputs_before = self.model(
+            input_ids=original_input_ids,
+            attention_mask=original_attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_before = outputs_before[0][:, -1:, :]
+
+        # two new tokens: last continuation token and end thought token
+        outputs_after = self.model(
+            input_ids=torch.cat([next_token_id.unsqueeze(-1).to(input_ids.device), torch.tensor(end_thought_token_id).unsqueeze(-1).unsqueeze(-1).to(input_ids.device)], dim=-1),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=new_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states_after = outputs_after[0][:, -1:, :]
+
+        # Apply the talk head to get the mixing weight
+        mixing_weight = self.talk_head[0](torch.cat([hidden_states_before, hidden_states_after], dim=-1))
+
+        # Apply the mixing weight to the hidden states
+        mixed_hidden_states = (1 - mixing_weight) * hidden_states_before + mixing_weight * hidden_states_after
+
+        # Apply the language model head to get the final logits
+        logits = self.lm_head(mixed_hidden_states)
+        return logits
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, QuietForCausalLM
+
+        >>> model = QuietForCausalLM.from_pretrained("quietai/Quiet-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("quietai/Quiet-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+        )
+        hidden_states = outputs.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        thought_ids, thought_embeddings = self._generate_thoughts(hidden_states, max_length=self.config.thought_length)
+        thought_hidden_states = self.model(inputs_embeds=thought_embeddings).last_hidden_state
+
+        # Compute thought logits
+        thought_logits = self.lm_head(thought_hidden_states)
+
+        # Mix base and thought logits
+        mixed_logits = logits.unsqueeze(1) + self.mixing_head(thought_logits)
+        mixed_logits = mixed_logits.view(-1, mixed_logits.size(-1))
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = mixed_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+            if self.use_policy_loss:
+                rewards = loss.detach().unsqueeze(1).repeat(1, self.max_thoughts)
+                if self.remove_negative_rewards:
+                    rewards = torch.clamp(rewards, min=0)
+                policy_loss = self.calculate_policy_loss(thought_ids, rewards)
+                loss = loss + policy_loss
+        else:
+            loss = None
+
+        if not return_dict:
+            output = (mixed_logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss if loss is not None else None,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing inputs_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+
+############################## Extra Heads #################################
+
+############# Sequence Classification ################# 
+@add_start_docstrings(
+    """
+    The Mistral Model transformer with a sequence classification head on top (linear layer).
+
+    [`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    MISTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mistral, LLAMA->MISTRAL
+class MistralForSequenceClassification(MistralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MistralModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+############# Token Classification    ################# 
+@add_start_docstrings(
+    """
+    The Mistral Model transformer with a token classification head on top (a linear layer on top of the hidden-states
+    output) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    MISTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->Mistral, LLAMA->MISTRAL
+class MistralForTokenClassification(MistralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MistralModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+############# QuestionAnswer    ################# 
+@add_start_docstrings(
+    """
+The Mistral Model transformer with a span classification head on top for extractive question-answering tasks like
+SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    MISTRAL_START_DOCSTRING,
+)
+class MistralForQuestionAnswering(MistralPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = MistralModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+############################## Closed Extra Heads ###########################
\ No newline at end of file