Delete 7f15baedd46858153d817445aff032f4d6cf4939

7f15baedd46858153d817445aff032f4d6cf4939/config.json

@@ -1,32 +0,0 @@
-{
-  "activation_function": "silu",
-  "architectures": [
-    "ExaoneForCausalLM"
-  ],
-  "attention_dropout": 0.0,
-  "auto_map": {
-    "AutoConfig": "configuration_exaone.ExaoneConfig",
-    "AutoModelForCausalLM": "modeling_exaone.ExaoneForCausalLM",
-    "AutoModelForSequenceClassification": "modeling_exaone.ExaoneForSequenceClassification"
-  },
-  "bos_token_id": 1,
-  "embed_dropout": 0.0,
-  "eos_token_id": 361,
-  "hidden_size": 4096,
-  "initializer_range": 0.02,
-  "intermediate_size": 14336,
-  "layer_norm_epsilon": 1e-05,
-  "max_position_embeddings": 4096,
-  "model_type": "exaone",
-  "num_attention_heads": 32,
-  "num_key_value_heads": 8,
-  "num_layers": 32,
-  "pad_token_id": 0,
-  "rope_scaling": null,
-  "rope_theta": 500000.0,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float32",
-  "transformers_version": "4.41.0",
-  "use_cache": true,
-  "vocab_size": 102400
-}

7f15baedd46858153d817445aff032f4d6cf4939/configuration_exaone.py

@@ -1,186 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The LG AI Research EXAONE Lab. All rights reserved.
-#
-# 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.
-""" EXAONE model configuration """
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-}
-
-
-class ExaoneConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.ExaoneModel`. It is used to
-    instantiate a EXAONE model according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the Exaone
-
-    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
-    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
-
-
-    Args:
-        vocab_size (:obj:`int`, `optional`, defaults to 102400):
-            Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the
-            :obj:`inputs_ids` passed when calling :class:`~transformers.ExaoneModel`. Vocabulary size of the model.
-            Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
-            :class:`~transformers.EXAONEModel`.
-        max_position_embeddings (:obj:`int`, `optional`, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        hidden_size (:obj:`int`, `optional`, defaults to 2048):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_layers (:obj:`int`, `optional`, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (:obj:`int`, `optional`, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (:obj:`int`, `optional`):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
-            `num_attention_heads`.
-        intermediate_size (:obj:`int`, `optional`, defaults to `hidden_size * 4`):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        activation_function (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        rope_theta (:obj:`float`, `optional`, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (:obj:`Dict`, `optional`):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (:obj:`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (:obj:`float`, `optional`):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (:obj:`int`, `optional`):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (:obj:`float`, `optional`):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (:obj:`float`, `optional`):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (:obj:`float`, `optional`):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (:obj:`List[float]`, `optional`):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (:obj:`List[float]`, `optional`):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (:obj:`float`, `optional`):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (:obj:`float`, `optional`):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        embed_dropout (:obj:`float`, `optional`, defaults to 0.0):
-            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_dropout (:obj:`float`, `optional`, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-5):
-            The epsilon used by the layer normalization layers.
-        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if ``config.is_decoder=True``.
-        bos_token_id (:obj:`int`, `optional`, defaults to 0):
-            Beginning of stream token id.
-        eos_token_id (:obj:`int`, `optional`, defaults to 2):
-            End of stream token id.
-        tie_word_embeddings (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether to tie weight embeddings
-        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
-
-        Example::
-
-            >>> from transformers import EXAONEModel, ExaoneConfig
-
-            >>> # Initializing a EXAONE configuration
-            >>> configuration = ExaoneConfig()
-
-            >>> # Initializing a model from configuration
-            >>> model = EXAONEModel(configuration)
-
-            >>> # Accessing the model configuration
-            >>> configuration = model.config
-    """
-    model_type = "exaone"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {"num_hidden_layers": "num_layers"}
-
-    def __init__(
-        self,
-        vocab_size=102400,
-        max_position_embeddings=2048,
-        hidden_size=2048,
-        num_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        intermediate_size=None,
-        activation_function="silu",
-        rope_theta=10000.0,
-        rope_scaling=None,
-        embed_dropout=0.0,
-        attention_dropout=0.0,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        use_cache=True,
-        bos_token_id=0,
-        eos_token_id=2,
-        tie_word_embeddings=True,
-        **kwargs
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.num_layers = num_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_hidden_layers = num_layers
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-        self.num_key_value_heads = num_key_value_heads
-        if intermediate_size:
-            self.intermediate_size = intermediate_size
-        else:
-            self.intermediate_size = hidden_size * 4
-        self.activation_function = activation_function
-        self.embed_dropout = embed_dropout
-        self.attention_dropout = attention_dropout
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        
-        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)

7f15baedd46858153d817445aff032f4d6cf4939/generation_config.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 1,
-  "eos_token_id": 361,
-  "pad_token_id": 0,
-  "transformers_version": "4.41"
-}

7f15baedd46858153d817445aff032f4d6cf4939/model-00001-of-00007.safetensors

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7f15baedd46858153d817445aff032f4d6cf4939/model.safetensors.index.json

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7f15baedd46858153d817445aff032f4d6cf4939/modeling_exaone.py

@@ -1,1747 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The LG AI Research EXAONE Lab
-# Copyright (c) 2018, NVIDIA CORPORATION.  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.
-""" LG AI Research EXAONE Lab"""
-import sys
-import os
-from typing import List, Optional, Tuple, Union
-from packaging import version
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-import torch.nn.functional as F
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    BaseModelOutputWithPastAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-    QuestionAnsweringModelOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    logging,
-)
-from .configuration_exaone import ExaoneConfig
-from torch.nn.utils import skip_init
-import math
-import numpy as np
-from typing import List, Optional, Tuple, Union
-
-
-if is_flash_attn_2_available():
-    try:
-        import inspect
-        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-        from flash_attn import flash_attn_func, flash_attn_varlen_func
-
-        _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
-
-        import flash_attn
-        if version.parse(flash_attn.__version__) > version.parse('2.4.2'):
-            from flash_attn.ops.triton.layer_norm import rms_norm_fn
-        else:
-            from flash_attn.ops.triton.layernorm import rms_norm_fn
-    except:
-        pass
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "exaone"
-_CONFIG_FOR_DOC = "ExaoneConfig"
-
-EXAONE_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "exaone",
-]
-
-
-@torch.jit.script
-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)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, 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.
-        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.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-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 llama
-def _prepare_4d_causal_attention_mask_with_cache_position(
-    attention_mask: torch.Tensor,
-    sequence_length: int,
-    target_length: int,
-    dtype: torch.dtype,
-    device: torch.device,
-    min_dtype: float,
-    cache_position: torch.Tensor,
-    batch_size: int,
-):
-    """
-    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-    Args:
-        attention_mask (`torch.Tensor`):
-            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
-        sequence_length (`int`):
-            The sequence length being processed.
-        target_length (`int`):
-            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
-        dtype (`torch.dtype`):
-            The dtype to use for the 4D attention mask.
-        device (`torch.device`):
-            The device to plcae the 4D attention mask on.
-        min_dtype (`float`):
-            The minimum value representable with the dtype `dtype`.
-        cache_position (`torch.Tensor`):
-            Indices depicting the position of the input sequence tokens in the sequence.
-        batch_size (`torch.Tensor`):
-            Batch size.
-    """
-    if attention_mask is not None and attention_mask.dim() == 4:
-        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-        causal_mask = attention_mask
-    else:
-        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-        if attention_mask is not None:
-            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-            mask_length = attention_mask.shape[-1]
-            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-            padding_mask = padding_mask == 0
-            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                padding_mask, min_dtype
-            )
-
-    return causal_mask
-
-
-class ExaoneRMSNorm(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.eps = eps
-        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
-
-    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.eps)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-class ExaoneTritonRMSNorm(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_size: int = 0,
-        eps: float = 1e-5,
-    ):
-        super().__init__()
-        self.eps = eps
-        self.drop = None
-        self.weight = torch.nn.Parameter(torch.empty(hidden_size))
-        self.register_parameter("bias", None)
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        torch.nn.init.ones_(self.weight)
-
-    def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False):
-        return rms_norm_fn(
-            x,
-            self.weight,
-            self.bias,
-            residual=residual,
-            eps=self.eps,
-            dropout_p=self.drop.p if self.drop is not None and self.training else 0.0,
-            prenorm=prenorm,
-            residual_in_fp32=residual_in_fp32,
-        )
-
-
-ALL_LAYERNORM_LAYERS.append(ExaoneRMSNorm)
-ALL_LAYERNORM_LAYERS.append(ExaoneTritonRMSNorm)
-
-
-class ExaoneRotaryEmbedding(nn.Module):
-    """
-    Common description for the functions named `_compute_XXX_rope_parameters()`
-        - Copied from `transformers.modeling_rope_utils` in v4.43, with some modifications.
-
-        Computes the inverse frequencies with linear scaling. 
-        The EXAONE model supports 'default', 'linear', 'dynamic', and 'yarn'.
-        
-        Args:
-            config (:obj:`~transformers.PretrainedConfig`):
-                The model configuration.
-            device (:obj:`torch.device`):
-                The device to use for initialization of the inverse frequencies.
-            seq_len (:obj:`int`, `optional`):
-                The current sequence length. Unused for this type of RoPE.
-        Returns:
-            Tuple of (:obj:`torch.Tensor`, :obj:`float`), containing the inverse frequencies for the RoPE embeddings and the
-            post-processing scaling factor applied to the computed cos/sin (unused in some types of RoPE).
-    """
-
-    def _compute_default_rope_parameters(
-        self,
-        config: Optional[PretrainedConfig],
-        device: Optional["torch.device"] = None,
-        seq_len: Optional[int] = None,
-    ) -> Tuple["torch.Tensor", float]:
-        base = config.rope_theta
-        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
-
-        attention_factor = 1.0  # Unused in this type of RoPE
-
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
-        return inv_freq, attention_factor
-
-    def _compute_linear_scaling_rope_parameters(
-        self,
-        config: Optional[PretrainedConfig],
-        device: Optional["torch.device"] = None,
-        seq_len: Optional[int] = None,
-    ) -> Tuple["torch.Tensor", float]:
-        factor = config.rope_scaling["factor"]
-        if factor < 1.0:
-            logger.warning_once(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
-
-        inv_freq, attention_factor = self._compute_default_rope_parameters(config, device, seq_len)
-        inv_freq /= factor
-        return inv_freq, attention_factor
-
-    def _compute_dynamic_ntk_parameters(
-        self,
-        config: Optional[PretrainedConfig],
-        device: Optional["torch.device"] = None,
-        seq_len: Optional[int] = None,
-    ) -> Tuple["torch.Tensor", float]:
-        base = config.rope_theta
-        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
-        max_position_embeddings = config.max_position_embeddings
-        factor = config.rope_scaling["factor"]
-        if factor < 1.0:
-            logger.warning_once(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
-
-        attention_factor = 1.0  # Unused in this type of RoPE
-        seq_len = seq_len if seq_len is not None else max_position_embeddings
-
-        base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
-        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
-        return inv_freq, attention_factor
-    
-    def _compute_yarn_parameters(
-        self,
-        config: PretrainedConfig, 
-        device: "torch.device", 
-        seq_len: Optional[int] = None, 
-    ) -> Tuple["torch.Tensor", float]:
-        base = config.rope_theta
-        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
-        max_position_embeddings = config.max_position_embeddings
-        factor = config.rope_scaling["factor"]
-        if factor < 1.0:
-            logger.warning_once(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
-
-        # Sets the attention factor as suggested in the paper
-        attention_factor = config.rope_scaling.get("attention_factor")
-        if attention_factor is None:
-            attention_factor = 0.1 * math.log(factor) + 1.0
-        if attention_factor < 0:
-            logger.warning_once(
-                f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
-            )
-
-        # Optional config options
-        # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
-        beta_fast = config.rope_scaling.get("beta_fast") or 32
-        beta_slow = config.rope_scaling.get("beta_slow") or 1
-        if not isinstance(beta_fast, float):
-            logger.warning_once(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
-        if not isinstance(beta_slow, float):
-            logger.warning_once(f"`rope_scaling`'s beta_slow field must be a float, got {beta_fast}")
-        if beta_fast < beta_slow:
-            logger.warning_once(
-                f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
-                f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
-            )
-
-        # Compute the inverse frequencies
-        def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
-            """Inverse dimension formula to find the dimension based on the number of rotations"""
-            return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
-
-        def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
-            """Find dimension range bounds based on rotations"""
-            low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
-            high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
-            return max(low, 0), min(high, dim - 1)
-
-        def linear_ramp_mask(min, max, dim):
-            if min == max:
-                max += 0.001  # Prevent singularity
-
-            linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
-            ramp_func = torch.clamp(linear_func, 0, 1)
-            return ramp_func
-
-        pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
-        inv_freq_extrapolation = 1.0 / pos_freqs
-        inv_freq_interpolation = 1.0 / (factor * pos_freqs)
-
-        low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
-
-        # Get n-dimensional rotational scaling corrected for extrapolation
-        inv_freq_mask = 1 - linear_ramp_mask(low, high, dim // 2).float().to(device)
-        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
-
-        return inv_freq, attention_factor
-
-    def __init__(self, config: ExaoneConfig, device=None):
-        ROPE_INIT_FUNCTIONS = {
-            "default": self._compute_default_rope_parameters,
-            "linear": self._compute_linear_scaling_rope_parameters,
-            "dynamic": self._compute_dynamic_ntk_parameters,
-            "yarn": self._compute_yarn_parameters,
-        }
-
-        super().__init__()
-        if config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        if self.rope_type not in ROPE_INIT_FUNCTIONS:
-            raise KeyError(f"The EXAONE model does not support RoPE type: {self.rope_type}")
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    def _update_freq(self, position_ids, device):
-        """
-        dynamic RoPE layers should recompute `inv_freq` in the following situations:
-        1 - growing beyond the cached sequence length (allow scaling)
-        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
-        """
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.max_seq_len:  # expand to seq_len
-            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
-            self.max_seq_len = seq_len
-        
-        if seq_len < self.original_max_seq_len and self.max_seq_len > self.original_max_seq_len:    # reset to original
-            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
-            self.max_seq_len = self.original_max_seq_len
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        if "dynamic" in self.rope_type:
-            self._update_freq(position_ids, device=x.device)
-
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos, sin = emb.cos(), emb.sin()
-        
-        cos, sin = cos * self.attention_scaling, sin * self.attention_scaling
-        return cos.to(x.dtype), sin.to(x.dtype)
-
-
-class ExaoneSelfAttention(nn.Module):
-    def __init__(self, config: ExaoneConfig, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // 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.attention_dropout_rate = config.attention_dropout
-
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
-            )
-
-        self.rotary = ExaoneRotaryEmbedding(config)
-        
-        self.k_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim, bias=False)
-        self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-
-        bsz, q_len, _ = hidden_states.size()
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        if position_embeddings is None:
-            cos, sin = self.rotary(value_states, position_ids=position_ids)
-        else:
-            cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attention_mask is not None:
-            causal_mask = attention_mask[:, :, :, :key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        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_rate, 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"Attention outputs 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.embed_dim).contiguous()
-
-        attn_output = self.out_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class ExaoneFlashAttention(ExaoneSelfAttention):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    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: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if isinstance(past_key_value, StaticCache):
-            raise ValueError(
-                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
-                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
-            )
-
-        output_attentions = False
-
-        bsz, q_len, h_size = 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)
-
-        if position_embeddings is None:
-            cos, sin = self.rotary(value_states, position_ids=position_ids)
-        else:
-            cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            # Only update cache as shape of [bsz, n_head, q_len, head_dim]
-            # TODO: need to be fixed when transformers' KV cache layout is changed
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        # 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 the correct dtype 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)
-
-        dropout_rate = self.attention_dropout_rate if self.training else 0.0
-
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, is_causal=True
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
-        attn_output = self.out_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-    
-    @staticmethod
-    def _flash_attention_forward(
-        query_states: torch.Tensor,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        query_length: int,
-        is_causal: bool,
-        dropout: float = 0.0,
-        softmax_scale: Optional[float] = None,
-        sliding_window: Optional[int] = None,
-        use_top_left_mask: bool = False,
-        softcap: Optional[float] = None,
-        deterministic: bool = os.environ.get("FLASH_ATTENTION_DETERMINISTIC", "0") == "1",
-    ):
-        """
-        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 (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-            use_top_left_mask (`bool`, defaults to `False`):
-                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.
-            softcap (`float`, *optional*):
-                Softcap for the attention logits, used e.g. in gemma2.
-            deterministic (`bool`, *optional*):
-                Determines if the deterministic option introduced in flash_attn>=2.4.1 is enabled.
-        """
-        if not use_top_left_mask:
-            causal = 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 transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__.
-            causal = is_causal and query_length != 1
-
-        # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length).
-        use_sliding_windows = (
-            _flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window
-        )
-        flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {}
-
-        if softcap is not None:
-            flash_kwargs["softcap"] = softcap
-
-        # 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 = ExaoneFlashAttention._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
-
-            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,
-                **flash_kwargs,
-            )
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, **flash_kwargs
-            )
-
-        return attn_output
-    
-    @staticmethod
-    def _upad_input(
-        query_layer: torch.Tensor,
-        key_layer: torch.Tensor,
-        value_layer: torch.Tensor,
-        attention_mask: torch.Tensor,
-        query_length: int,
-    ):
-        """
-        Unpads query, key, and values tensors, using a single dimension for all tokens even though they belong to different batches.
-
-        This function is used instead of `flash_attn.bert_padding.unpad_input` in order to avoid the recomputation of the same intermediary
-        tensors for query, key, value tensors.
-
-        Arguments:
-            query_layer (`torch.Tensor`):
-                Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
-            key_layer (`torch.Tensor`):
-                Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
-            value_layer (`torch.Tensor`):
-                Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
-            attention_mask (`torch.Tensor`):
-                Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
-            query_length (`int`):
-                Target length.
-
-        Return:
-            query_layer (`torch.Tensor):
-                Query state without padding. Shape: (total_target_length, num_heads, head_dim).
-            key_layer (`torch.Tensor`):
-                Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
-            value_layer (`torch.Tensor`):
-                Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
-            indices_q (`torch.Tensor`):
-                The indices of non-masked tokens from the flattened input target sequence.
-            (cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
-                The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
-                Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
-        """
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = ExaoneFlashAttention._get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, -1, 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),
-        )
-    
-    @staticmethod
-    def _get_unpad_data(attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, int]:
-        """
-        Retrieves indexing data required to repad unpadded (ragged) tensors.
-
-        Arguments:
-            attention_mask (`torch.Tensor`):
-                Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
-
-        Return:
-            indices (`torch.Tensor):
-                The indices of non-masked tokens from the flattened input sequence.
-            cu_seqlens (`torch.Tensor`):
-                The cumulative sequence lengths, used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
-            max_seqlen_in_batch (`int`):
-                Maximum sequence length in batch.
-        """
-        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.int32), (1, 0))
-        return (
-            indices,
-            cu_seqlens,
-            max_seqlen_in_batch,
-        )
-
-
-class ExaoneSdpaAttention(ExaoneSelfAttention):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-    
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-
-        if output_attentions:
-            logger.warning_once(
-                "ExaoneModel is using ExaoneSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_value,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **kwargs,
-            )
-
-        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)
-
-        if position_embeddings is None:
-            cos, sin = self.rotary(value_states, position_ids=position_ids)
-        else:
-            cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        causal_mask = attention_mask
-        if attention_mask is not None:
-            causal_mask = causal_mask[:, :, :, :key_states.shape[-2]]        
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
-        # Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and causal_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
-        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
-        is_causal = True if causal_mask is None and q_len > 1 else False
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=causal_mask,
-            dropout_p=self.attention_dropout_rate if self.training else 0.0,
-            is_causal=is_causal,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous()
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-class ExaoneAttention(nn.Module):
-    def __init__(self, config, layer_id=0):
-        super().__init__()
-        self.layer_id = layer_id
-        if 'flash' in config._attn_implementation:
-            self.attention = ExaoneFlashAttention(config, self.layer_id)
-        elif 'sdpa' in config._attn_implementation:
-            self.attention = ExaoneSdpaAttention(config, self.layer_id)
-        else:
-            self.attention = ExaoneSelfAttention(config, self.layer_id)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-
-        return self.attention(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-
-
-class ExaoneGatedMLP(nn.Module):
-    def __init__(self, intermediate_size, config):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-        self.c_fc_0 = nn.Linear(embed_dim, intermediate_size, bias=False)
-        self.c_fc_1 = nn.Linear(embed_dim, intermediate_size, bias=False)
-        self.c_proj = nn.Linear(intermediate_size, embed_dim, bias=False)
-        self.act = ACT2FN[config.activation_function]
-
-    def forward(self, hidden_states):
-        output_proj = self.c_proj(self.act(self.c_fc_0(hidden_states)) * self.c_fc_1(hidden_states))
-        return output_proj
-
-
-class ExaoneBlock(nn.Module):
-    def __init__(self, config, layer_id):
-        super().__init__()
-        self.config = config
-        hidden_size = config.hidden_size
-        inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size
-        self.ln_1 = ExaoneRMSNorm(hidden_size = hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = ExaoneAttention(config, layer_id)
-        self.ln_2 = ExaoneRMSNorm(hidden_size = hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = ExaoneGatedMLP(inner_dim, config)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-
-        residual = hidden_states
-        hidden_states = self.ln_1(hidden_states)
-
-        hidden_states, self_attn_weights, present_key_value = self.attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        # residual connection
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.ln_2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-class ExaonePreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = ExaoneConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["ExaoneBlock"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, (nn.Linear,)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, ExaoneRMSNorm):
-            module.weight.data.fill_(1.0)
-
-
-EXAONE_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 (:class:`~transformers.ExaoneConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
-"""
-
-EXAONE_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
-            :obj:`input_ids_length` = ``sequence_length`` if :obj:`past_key_values` is ``None`` else
-            ``past_key_values.get_seq_length()`` (``sequence_length`` of input past key value states). Indices of input
-            sequence tokens in the vocabulary.
-
-            If :obj:`past_key_values` is used, only ``input_ids`` that do not have their past calculated should be
-            passed as ``input_ids``.
-
-            `What are input IDs? <../glossary.html#input-ids>`__
-        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(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.html#attention-mask>`__
-        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
-            config.max_position_embeddings - 1]``.
-
-            `What are position IDs? <../glossary.html#position-ids>`_
-        past_key_values (:obj:`Cache`, `optional`):
-            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
-            :obj:`past_key_values` output below). 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`.
-        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
-            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
-            vectors than the model's internal embedding lookup matrix.
-
-            If :obj:`past_key_values` is used, optionally only the last :obj:`inputs_embeds` have to be input (see
-            :obj:`past_key_values`).
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        output_attentions (:obj:`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 (:obj:`bool`, `optional`):
-            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
-            more detail.
-        return_dict (:obj:`bool`, `optional`):
-            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
-        cache_position (:obj:`torch.LongTensor` of shape :obj:`(sequence_length)`, `optional`):
-            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
-            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
-            the complete sequence length.
-"""
-
-
-@add_start_docstrings(
-    "The bare EXAONE Model transformer outputting raw hidden-states without any specific head on top.",
-    EXAONE_START_DOCSTRING,
-)
-class ExaoneModel(ExaonePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.wte = nn.Embedding(config.vocab_size, self.embed_dim, self.config.pad_token_id)
-        self.drop = nn.Dropout(float(config.embed_dropout))
-        self.h = nn.ModuleList([ExaoneBlock(config, layer_id=i) for i in range(config.num_layers)])
-        self.ln_f = ExaoneRMSNorm(hidden_size=self.embed_dim, eps=config.layer_norm_epsilon)
-        self.rotary = ExaoneRotaryEmbedding(config)
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.wte
-
-    def set_input_embeddings(self, new_embeddings):
-        self.wte = new_embeddings
-
-    @add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPastAndCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple[torch.Tensor], 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
-
-        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
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        return_legacy_cache = False
-        if (
-            use_cache and not isinstance(past_key_values, Cache) and not self.training
-        ):  # kept for BC (non `Cache` `past_key_values` inputs)
-            return_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            logger.warning_once(
-                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
-                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
-            )
-
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        hidden_states = inputs_embeds
-        hidden_states = self.drop(hidden_states)
-
-        position_embeddings = self.rotary(hidden_states, position_ids)
-
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for block in self.h:
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                outputs = self._gradient_checkpointing_func(
-                    block.__call__,
-                    hidden_states,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                    position_embeddings,
-                )
-            else:
-                outputs = block(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                    position_embeddings=position_embeddings,
-                )
-
-            hidden_states = outputs[0]
-            if use_cache:
-                next_decoder_cache = outputs[2 if output_attentions else 1]
-
-            if output_attentions:
-                all_self_attns += (outputs[1],)
-
-        hidden_states = self.ln_f(hidden_states)
-        # Add last hidden state
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if return_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,
-        )
-    
-    # copied from llama
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
-    ):
-        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
-        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
-        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
-        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
-
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
-        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
-        # to infer the attention mask.
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                is_training=self.training,
-            ):
-                return None
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        if using_static_cache:
-            target_length = past_key_values.get_max_length()
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            device=device,
-            min_dtype=min_dtype,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-        )
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type == "cuda"
-            and not output_attentions
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
-
-        return causal_mask
-
-
-@add_start_docstrings(
-    """
-    The EXAONE Model transformer with a language modeling head on top (linear layer with weights tied to the input
-    embeddings).
-    """,
-    EXAONE_START_DOCSTRING,
-)
-class ExaoneForCausalLM(ExaonePreTrainedModel):
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = ExaoneModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.config = config
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    @add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
-                `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
-                are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoModelForCausalLM, AutoTokenizer
-
-        >>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
-                                                         trust_remote_code=True)
-        >>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct")
-
-        >>> prompt = "Explain how wonderful you are"
-        >>> messages = [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": prompt}
-        ]
-        >>> input_ids = tokenizer.apply_chat_template(
-            messages,
-            tokenize=True,
-            add_generation_prompt=True,
-            return_tensors="pt"
-        )
-
-        >>> output = model.generate(input_ids, max_new_tokens=128)
-        >>> tokenizer.decode(output[0], skip_special_tokens=True)
-        "[|system|]You are a helpful assistant.\n[|user|]Explain how wonderful you are\n[|assistant|]Thank you for your kind words! I'm here to assist you with information, answer questions, and help you in any way I can. My goal is to provide accurate, helpful, and timely responses. Whether you need help with a specific task, want to learn something new, or just need someone to talk to, I'm here for you. How can I assist you today?"
-        ```
-        """
-
-        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
-        transformer_outputs = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            past_key_values=past_key_values,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
-        lm_logits = lm_logits.float()
-        loss = None
-        if labels is not None:
-            lm_logits = lm_logits.to(torch.float32)
-
-            # Shift so that tokens < n predict n
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-            lm_logits = lm_logits.to(hidden_states.dtype)
-            loss = loss.to(hidden_states.dtype)
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        cache_position=None,
-        position_ids=None,
-        use_cache=True,
-        **kwargs,
-    ):
-        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
-        # Exception 1: when passing input_embeds, input_ids may be missing entries
-        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
-        if past_key_values is not None:
-            if inputs_embeds is not None:  # Exception 1
-                input_ids = input_ids[:, -cache_position.shape[0] :]
-            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
-                input_ids = input_ids[:, cache_position]
-
-        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] :]
-
-                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
-                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and cache_position[0] == 0:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
-            if inputs_embeds is not None:
-                batch_size, sequence_length = inputs_embeds.shape
-                device = inputs_embeds.device
-            else:
-                batch_size, sequence_length = input_ids.shape
-                device = input_ids.device
-
-            dtype = self.lm_head.weight.dtype
-            min_dtype = torch.finfo(dtype).min
-
-            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
-                attention_mask,
-                sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
-                dtype=dtype,
-                device=device,
-                min_dtype=min_dtype,
-                cache_position=cache_position,
-                batch_size=batch_size,
-            )
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "cache_position": cache_position,
-                "past_key_values": past_key_values,
-                "use_cache": 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 EXAONE Model transformer with a sequence classification head on top (linear layer).
-
-    :class:`~transformers.ExaoneForSequenceClassification` uses the last token in order to do the classification, as
-    other causal models (e.g. GPT-1) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    :obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
-    row. If no :obj:`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 :obj:`inputs_embeds` are passed instead of :obj:`input_ids`, it does the same (take
-    the last value in each row of the batch).
-    """,
-    EXAONE_START_DOCSTRING,
-)
-class ExaoneForSequenceClassification(ExaonePreTrainedModel):
-    _keys_to_ignore_on_load_missing = ["lm_head.weight"]
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.transformer = ExaoneModel(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutputWithPast,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], 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.transformer(
-            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, sequence_length = input_ids.shape[:2]
-        else:
-            batch_size, sequence_length = inputs_embeds.shape[:2]
-
-        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.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.to(logits.device)
-            else:
-                sequence_lengths = -1
-                logger.warning(
-                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-                )
-
-        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,
-        )
-
-
-@add_start_docstrings(
-    """
-    The EXAONE Model transformer with a span classification head on top for extractive question-answering tasks like
-    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    EXAONE_START_DOCSTRING,
-)
-class ExaoneForQuestionAnswering(ExaonePreTrainedModel):
-    _keys_to_ignore_on_load_missing = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.transformer = ExaoneModel(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Model parallel
-        self.model_parallel = False
-        self.device_map = None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
-        r"""
-        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
-            sequence are not taken into account for computing the loss.
-        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
-            sequence are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1).to(start_logits.device)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1).to(end_logits.device)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )