diff --git "a/modeling_quiet.py" "b/modeling_quiet.py"
new file mode 100644--- /dev/null
+++ "b/modeling_quiet.py"
@@ -0,0 +1,2379 @@
+# coding=utf-8
+# Copyright 2023 Quiet AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Quiet model."""
+import inspect
+import math
+import copy
+import os
+import time
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import wandb
+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
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+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,
+)
+from .configuration_quiet import QuietConfig
+
+
+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)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "QuietConfig"
+
+from reportlab.pdfgen import canvas
+from reportlab.lib.pagesizes import letter
+from reportlab.lib.colors import HexColor
+
+def save_tokens_with_rewards_to_pdf(input_ids, token_rewards, tokenizer, output_file="text.pdf", eps=0.2, eps2=0.5):
+    c = canvas.Canvas(output_file, pagesize=letter)
+    c.setFont("Courier", 8)
+    x, y = 50, 750
+    previous_text = ""
+    current_text = ""
+    for token_idx, reward in enumerate(token_rewards):
+        current_text = tokenizer.decode(input_ids[: token_idx + 1])
+        if current_text != previous_text:
+            diff_text = current_text[len(previous_text) :]
+            if "\n" in diff_text:
+                lines = diff_text.split("\n")
+                for line_idx, line in enumerate(lines):
+                    if line_idx > 0:
+                        x = 50
+                        y -= 12
+                    if abs(reward) < eps:
+                        opacity = 0
+                    elif abs(reward) > eps2:
+                        opacity = 0.8
+                    else:
+                        opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps)
+                    text_width = c.stringWidth(line)
+                    if reward > 0:
+                        highlight_color = HexColor("#4CCD99")
+                    else:
+                        highlight_color = HexColor("#FFC700")
+                    highlight_color.alpha = opacity
+                    c.setFillColor(highlight_color)
+                    c.rect(x, y - 2, text_width, 10, fill=True, stroke=False)
+                    c.setFillColor(HexColor("#000000"))
+                    c.drawString(x, y, line)
+                    x += text_width
+            else:
+                if abs(reward) < eps:
+                    opacity = 0
+                elif abs(reward) > eps2:
+                    opacity = 0.8
+                else:
+                    opacity = 0.8 * (abs(reward) - eps) / (eps2 - eps)
+                text_width = c.stringWidth(diff_text)
+                if reward > 0:
+                    highlight_color = HexColor("#4CCD99")
+                else:
+                    highlight_color = HexColor("#FFC700")
+                highlight_color.alpha = opacity
+                c.setFillColor(highlight_color)
+                c.rect(x, y - 2, text_width, 10, fill=True, stroke=False)
+                c.setFillColor(HexColor("#000000"))
+                c.drawString(x, y, diff_text)
+                x += text_width
+            if x > 550:
+                x = 50
+                y -= 12
+            if y < 50:
+                c.showPage()
+                y = 750
+                x = 50
+            previous_text = current_text
+    c.showPage()
+    c.save()
+
+
+# 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,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Quiet
+class QuietRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        QuietRMSNorm 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 hidden_states.to(input_dtype) * self.weight.to(hidden_states.device)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Quiet
+class QuietRotaryEmbedding(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).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# 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, 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`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        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.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class QuietMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        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, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# 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 QuietAttention(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: QuietConfig, 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 `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        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
+        self.attention_dropout = config.attention_dropout
+
+        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.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = QuietRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    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,
+        **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)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        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)
+
+        # 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)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if 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" {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()}"
+                )
+
+            attn_weights = attn_weights + attention_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.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
+
+
+class QuietFlashAttention2(QuietAttention):
+    """
+    Quiet flash attention module. This module inherits from `QuietAttention` 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)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # 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,
+        **kwargs,
+    ):
+        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.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+        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)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        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 = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        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
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Quiet
+class QuietSdpaAttention(QuietAttention):
+    """
+    Quiet attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `QuietAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from QuietAttention.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,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "QuietModel is using QuietSdpaAttention, 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,
+            )
+
+        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 += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        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)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        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()}"
+                )
+
+        # 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 attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask.to(query_states.device) if attention_mask is not None else None,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        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)
+
+        return attn_output, None, past_key_value
+
+
+QUIET_ATTENTION_CLASSES = {
+    "eager": QuietAttention,
+    "flash_attention_2": QuietFlashAttention2,
+    "sdpa": QuietSdpaAttention,
+}
+
+
+class QuietDecoderLayer(nn.Module):
+    def __init__(self, config: QuietConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = QUIET_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+        self.mlp = QuietMLP(config)
+        self.input_layernorm = QuietRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = QuietRMSNorm(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,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        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.`"
+            )
+        """
+        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.
+            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
+        """
+
+        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,
+        )
+        hidden_states = residual.to(hidden_states.device) + 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
+
+
+QUIET_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 ([`QuietConfig`]):
+            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.
+"""
+
+
+@add_start_docstrings(
+    "The bare Quiet Model outputting raw hidden-states without any specific head on top.",
+    QUIET_START_DOCSTRING,
+)
+class QuietPreTrainedModel(PreTrainedModel):
+    config_class = QuietConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["QuietDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = 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_()
+
+
+QUIET_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.
+"""
+
+
+@add_start_docstrings(
+    "The bare Quiet Model outputting raw hidden-states without any specific head on top.",
+    QUIET_START_DOCSTRING,
+)
+class QuietModel(QuietPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`QuietDecoderLayer`]
+
+    Args:
+        config: QuietConfig
+    """
+
+    def __init__(self, config: QuietConfig):
+        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(
+            [QuietDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = QuietRMSNorm(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(QUIET_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[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,
+    ) -> 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 not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+
+        if use_cache:
+            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_length)
+
+        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_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            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 Quiet. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        if self._attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._attn_implementation == "sdpa" and not output_attentions and attention_mask.dim() == 2 and False:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+        elif attention_mask is None or attention_mask.dim() == 2:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        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,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            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 = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_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 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()
+
+class QuietForCausalLM(QuietPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = QuietModel(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.wandb_enabled = False
+        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
+
+    @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(QUIET_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."
+        ```"""
+        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,
+        )
+
+
+    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
+
+
+@add_start_docstrings(
+    """
+    The Quiet Model transformer with a sequence classification head on top (linear layer).
+
+    [`QuietForSequenceClassification`] 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).
+    """,
+    QUIET_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Quiet, LLAMA->QUIET
+class QuietForSequenceClassification(QuietPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = QuietModel(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(QUIET_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[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,
+        )
\ No newline at end of file