Add files using upload-large-folder tool

README.md

@@ -0,0 +1,45 @@
+---
+base_model: LGAI-EXAONE/EXAONE-Deep-2.4B
+base_model_relation: finetune
+license: other
+license_name: exaone
+license_link: LICENSE
+language:
+- en
+- ko
+tags:
+- lg-ai
+- exaone
+- exaone-deep
+- mlx
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# alexgusevski/EXAONE-Deep-2.4B-q8-mlx
+
+The Model [alexgusevski/EXAONE-Deep-2.4B-q8-mlx](https://huggingface.co/alexgusevski/EXAONE-Deep-2.4B-q8-mlx) was
+converted to MLX format from [LGAI-EXAONE/EXAONE-Deep-2.4B](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B)
+using mlx-lm version **0.22.0**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("alexgusevski/EXAONE-Deep-2.4B-q8-mlx")
+
+prompt = "hello"
+
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```

config.json

@@ -0,0 +1,47 @@
+{
+    "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,
+    "head_dim": 80,
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 7168,
+    "layer_norm_epsilon": 1e-05,
+    "max_position_embeddings": 32768,
+    "model_type": "exaone",
+    "num_attention_heads": 32,
+    "num_key_value_heads": 8,
+    "num_layers": 30,
+    "pad_token_id": 0,
+    "quantization": {
+        "group_size": 64,
+        "bits": 8
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 8
+    },
+    "rope_scaling": {
+        "factor": 8.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "rope_theta": 1000000,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.43.1",
+    "use_cache": true,
+    "vocab_size": 102400
+}

configuration_exaone.py

@@ -0,0 +1,183 @@
+# 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 [`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-3.0-7.8B-Instruct [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
+    outputs. Read the documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 102400):
+            Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ExaoneModel`]. Vocabulary size of the model.
+            Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
+            [`ExaoneModel`].
+        max_position_embeddings (`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 (`int`, *optional*, defaults to 2048):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`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 (`int`, *optional*, defaults to `hidden_size * 4`):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        activation_function (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`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` (`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` (`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` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`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` (`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` (`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` (`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` (`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` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        embed_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if ``config.is_decoder=True``.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+
+    Example:
+
+    ```python
+    >>> 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,
+        **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_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, **kwargs)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d3360f20f1c3aa3a58413ecf9da62d43e00d314afcf71387020743316a53b2e
+size 2555886414

model.safetensors.index.json

@@ -0,0 +1,701 @@
+{
+    "metadata": {
+        "total_size": 2555806720
+    },
+    "weight_map": {
+        "transformer.h.0.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.0.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.0.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.0.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.0.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.0.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.0.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.0.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.0.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.0.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.0.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.0.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.0.ln_1.weight": "model.safetensors",
+        "transformer.h.0.ln_2.weight": "model.safetensors",
+        "transformer.h.0.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.0.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.0.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.0.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.0.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.0.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.0.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.0.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.0.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.1.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.1.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.1.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.1.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.1.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.1.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.1.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.1.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.1.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.1.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.1.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.1.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.1.ln_1.weight": "model.safetensors",
+        "transformer.h.1.ln_2.weight": "model.safetensors",
+        "transformer.h.1.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.1.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.1.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.1.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.1.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.1.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.1.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.1.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.1.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.10.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.10.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.10.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.10.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.10.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.10.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.10.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.10.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.10.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.10.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.10.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.10.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.10.ln_1.weight": "model.safetensors",
+        "transformer.h.10.ln_2.weight": "model.safetensors",
+        "transformer.h.10.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.10.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.10.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.10.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.10.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.10.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.10.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.10.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.10.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.11.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.11.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.11.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.11.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.11.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.11.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.11.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.11.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.11.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.11.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.11.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.11.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.11.ln_1.weight": "model.safetensors",
+        "transformer.h.11.ln_2.weight": "model.safetensors",
+        "transformer.h.11.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.11.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.11.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.11.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.11.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.11.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.11.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.11.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.11.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.12.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.12.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.12.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.12.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.12.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.12.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.12.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.12.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.12.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.12.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.12.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.12.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.12.ln_1.weight": "model.safetensors",
+        "transformer.h.12.ln_2.weight": "model.safetensors",
+        "transformer.h.12.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.12.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.12.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.12.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.12.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.12.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.12.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.12.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.12.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.13.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.13.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.13.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.13.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.13.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.13.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.13.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.13.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.13.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.13.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.13.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.13.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.13.ln_1.weight": "model.safetensors",
+        "transformer.h.13.ln_2.weight": "model.safetensors",
+        "transformer.h.13.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.13.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.13.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.13.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.13.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.13.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.13.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.13.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.13.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.14.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.14.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.14.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.14.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.14.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.14.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.14.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.14.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.14.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.14.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.14.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.14.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.14.ln_1.weight": "model.safetensors",
+        "transformer.h.14.ln_2.weight": "model.safetensors",
+        "transformer.h.14.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.14.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.14.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.14.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.14.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.14.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.14.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.14.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.14.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.15.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.15.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.15.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.15.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.15.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.15.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.15.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.15.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.15.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.15.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.15.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.15.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.15.ln_1.weight": "model.safetensors",
+        "transformer.h.15.ln_2.weight": "model.safetensors",
+        "transformer.h.15.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.15.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.15.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.15.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.15.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.15.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.15.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.15.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.15.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.16.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.16.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.16.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.16.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.16.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.16.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.16.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.16.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.16.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.16.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.16.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.16.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.16.ln_1.weight": "model.safetensors",
+        "transformer.h.16.ln_2.weight": "model.safetensors",
+        "transformer.h.16.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.16.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.16.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.16.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.16.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.16.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.16.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.16.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.16.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.17.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.17.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.17.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.17.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.17.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.17.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.17.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.17.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.17.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.17.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.17.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.17.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.17.ln_1.weight": "model.safetensors",
+        "transformer.h.17.ln_2.weight": "model.safetensors",
+        "transformer.h.17.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.17.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.17.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.17.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.17.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.17.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.17.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.17.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.17.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.18.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.18.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.18.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.18.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.18.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.18.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.18.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.18.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.18.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.18.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.18.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.18.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.18.ln_1.weight": "model.safetensors",
+        "transformer.h.18.ln_2.weight": "model.safetensors",
+        "transformer.h.18.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.18.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.18.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.18.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.18.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.18.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.18.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.18.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.18.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.19.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.19.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.19.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.19.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.19.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.19.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.19.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.19.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.19.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.19.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.19.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.19.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.19.ln_1.weight": "model.safetensors",
+        "transformer.h.19.ln_2.weight": "model.safetensors",
+        "transformer.h.19.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.19.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.19.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.19.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.19.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.19.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.19.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.19.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.19.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.2.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.2.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.2.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.2.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.2.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.2.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.2.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.2.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.2.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.2.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.2.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.2.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.2.ln_1.weight": "model.safetensors",
+        "transformer.h.2.ln_2.weight": "model.safetensors",
+        "transformer.h.2.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.2.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.2.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.2.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.2.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.2.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.2.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.2.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.2.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.20.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.20.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.20.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.20.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.20.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.20.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.20.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.20.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.20.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.20.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.20.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.20.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.20.ln_1.weight": "model.safetensors",
+        "transformer.h.20.ln_2.weight": "model.safetensors",
+        "transformer.h.20.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.20.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.20.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.20.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.20.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.20.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.20.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.20.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.20.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.21.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.21.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.21.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.21.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.21.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.21.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.21.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.21.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.21.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.21.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.21.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.21.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.21.ln_1.weight": "model.safetensors",
+        "transformer.h.21.ln_2.weight": "model.safetensors",
+        "transformer.h.21.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.21.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.21.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.21.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.21.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.21.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.21.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.21.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.21.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.22.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.22.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.22.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.22.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.22.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.22.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.22.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.22.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.22.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.22.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.22.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.22.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.22.ln_1.weight": "model.safetensors",
+        "transformer.h.22.ln_2.weight": "model.safetensors",
+        "transformer.h.22.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.22.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.22.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.22.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.22.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.22.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.22.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.22.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.22.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.23.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.23.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.23.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.23.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.23.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.23.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.23.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.23.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.23.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.23.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.23.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.23.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.23.ln_1.weight": "model.safetensors",
+        "transformer.h.23.ln_2.weight": "model.safetensors",
+        "transformer.h.23.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.23.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.23.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.23.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.23.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.23.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.23.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.23.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.23.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.24.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.24.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.24.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.24.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.24.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.24.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.24.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.24.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.24.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.24.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.24.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.24.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.24.ln_1.weight": "model.safetensors",
+        "transformer.h.24.ln_2.weight": "model.safetensors",
+        "transformer.h.24.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.24.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.24.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.24.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.24.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.24.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.24.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.24.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.24.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.25.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.25.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.25.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.25.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.25.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.25.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.25.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.25.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.25.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.25.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.25.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.25.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.25.ln_1.weight": "model.safetensors",
+        "transformer.h.25.ln_2.weight": "model.safetensors",
+        "transformer.h.25.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.25.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.25.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.25.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.25.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.25.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.25.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.25.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.25.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.26.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.26.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.26.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.26.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.26.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.26.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.26.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.26.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.26.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.26.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.26.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.26.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.26.ln_1.weight": "model.safetensors",
+        "transformer.h.26.ln_2.weight": "model.safetensors",
+        "transformer.h.26.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.26.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.26.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.26.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.26.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.26.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.26.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.26.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.26.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.27.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.27.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.27.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.27.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.27.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.27.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.27.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.27.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.27.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.27.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.27.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.27.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.27.ln_1.weight": "model.safetensors",
+        "transformer.h.27.ln_2.weight": "model.safetensors",
+        "transformer.h.27.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.27.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.27.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.27.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.27.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.27.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.27.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.27.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.27.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.28.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.28.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.28.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.28.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.28.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.28.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.28.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.28.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.28.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.28.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.28.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.28.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.28.ln_1.weight": "model.safetensors",
+        "transformer.h.28.ln_2.weight": "model.safetensors",
+        "transformer.h.28.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.28.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.28.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.28.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.28.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.28.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.28.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.28.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.28.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.29.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.29.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.29.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.29.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.29.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.29.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.29.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.29.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.29.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.29.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.29.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.29.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.29.ln_1.weight": "model.safetensors",
+        "transformer.h.29.ln_2.weight": "model.safetensors",
+        "transformer.h.29.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.29.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.29.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.29.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.29.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.29.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.29.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.29.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.29.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.3.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.3.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.3.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.3.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.3.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.3.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.3.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.3.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.3.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.3.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.3.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.3.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.3.ln_1.weight": "model.safetensors",
+        "transformer.h.3.ln_2.weight": "model.safetensors",
+        "transformer.h.3.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.3.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.3.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.3.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.3.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.3.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.3.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.3.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.3.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.4.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.4.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.4.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.4.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.4.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.4.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.4.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.4.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.4.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.4.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.4.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.4.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.4.ln_1.weight": "model.safetensors",
+        "transformer.h.4.ln_2.weight": "model.safetensors",
+        "transformer.h.4.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.4.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.4.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.4.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.4.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.4.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.4.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.4.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.4.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.5.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.5.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.5.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.5.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.5.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.5.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.5.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.5.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.5.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.5.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.5.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.5.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.5.ln_1.weight": "model.safetensors",
+        "transformer.h.5.ln_2.weight": "model.safetensors",
+        "transformer.h.5.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.5.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.5.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.5.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.5.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.5.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.5.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.5.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.5.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.6.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.6.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.6.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.6.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.6.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.6.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.6.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.6.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.6.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.6.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.6.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.6.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.6.ln_1.weight": "model.safetensors",
+        "transformer.h.6.ln_2.weight": "model.safetensors",
+        "transformer.h.6.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.6.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.6.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.6.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.6.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.6.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.6.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.6.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.6.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.7.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.7.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.7.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.7.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.7.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.7.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.7.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.7.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.7.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.7.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.7.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.7.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.7.ln_1.weight": "model.safetensors",
+        "transformer.h.7.ln_2.weight": "model.safetensors",
+        "transformer.h.7.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.7.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.7.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.7.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.7.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.7.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.7.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.7.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.7.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.8.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.8.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.8.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.8.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.8.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.8.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.8.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.8.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.8.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.8.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.8.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.8.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.8.ln_1.weight": "model.safetensors",
+        "transformer.h.8.ln_2.weight": "model.safetensors",
+        "transformer.h.8.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.8.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.8.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.8.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.8.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.8.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.8.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.8.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.8.mlp.c_proj.weight": "model.safetensors",
+        "transformer.h.9.attn.attention.k_proj.biases": "model.safetensors",
+        "transformer.h.9.attn.attention.k_proj.scales": "model.safetensors",
+        "transformer.h.9.attn.attention.k_proj.weight": "model.safetensors",
+        "transformer.h.9.attn.attention.out_proj.biases": "model.safetensors",
+        "transformer.h.9.attn.attention.out_proj.scales": "model.safetensors",
+        "transformer.h.9.attn.attention.out_proj.weight": "model.safetensors",
+        "transformer.h.9.attn.attention.q_proj.biases": "model.safetensors",
+        "transformer.h.9.attn.attention.q_proj.scales": "model.safetensors",
+        "transformer.h.9.attn.attention.q_proj.weight": "model.safetensors",
+        "transformer.h.9.attn.attention.v_proj.biases": "model.safetensors",
+        "transformer.h.9.attn.attention.v_proj.scales": "model.safetensors",
+        "transformer.h.9.attn.attention.v_proj.weight": "model.safetensors",
+        "transformer.h.9.ln_1.weight": "model.safetensors",
+        "transformer.h.9.ln_2.weight": "model.safetensors",
+        "transformer.h.9.mlp.c_fc_0.biases": "model.safetensors",
+        "transformer.h.9.mlp.c_fc_0.scales": "model.safetensors",
+        "transformer.h.9.mlp.c_fc_0.weight": "model.safetensors",
+        "transformer.h.9.mlp.c_fc_1.biases": "model.safetensors",
+        "transformer.h.9.mlp.c_fc_1.scales": "model.safetensors",
+        "transformer.h.9.mlp.c_fc_1.weight": "model.safetensors",
+        "transformer.h.9.mlp.c_proj.biases": "model.safetensors",
+        "transformer.h.9.mlp.c_proj.scales": "model.safetensors",
+        "transformer.h.9.mlp.c_proj.weight": "model.safetensors",
+        "transformer.ln_f.weight": "model.safetensors",
+        "transformer.wte.biases": "model.safetensors",
+        "transformer.wte.scales": "model.safetensors",
+        "transformer.wte.weight": "model.safetensors"
+    }
+}

modeling_exaone.py

@@ -0,0 +1,1394 @@
+# 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 math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_flash_attention_utils import _flash_attention_forward
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    BaseModelOutputWithPastAndCrossAttentions,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+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
+
+
+if is_flash_attn_2_available():
+    try:
+        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 ImportError:
+        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)
+
+
+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):
+    def __init__(self, config: ExaoneConfig, device=None):
+        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.rope_theta = config.rope_theta
+        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).to(x.device)
+        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 = _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
+
+
+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 ([`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 `PreTrainedModel.from_pretrained` method to load the model weights.
+"""
+
+EXAONE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
+            `input_ids_length` = `sequence_length` if `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 `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 (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            `What are position IDs? <../glossary.html#position-ids>`_
+        past_key_values (`Cache`, *optional*):
+            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
+            `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 (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert `input_ids` indices into associated
+            vectors than the model's internal embedding lookup matrix.
+
+            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
+            `past_key_values`).
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up
+            decoding (see `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(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,
+        )
+
+    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, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    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, "input_ids": None}
+        else:
+            model_inputs = {"input_ids": input_ids, "inputs_embeds": None}
+
+        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
+
+
+@add_start_docstrings(
+    """
+    The EXAONE Model transformer with a sequence classification head on top (linear layer).
+
+    [`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
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
+    row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
+    guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take
+    the last value in each row of the batch).
+    """,
+    EXAONE_START_DOCSTRING,
+)
+class ExaoneForSequenceClassification(ExaonePreTrainedModel):
+    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):
+    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 (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "[BOS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[|endofturn|]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,3222 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "                               ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "5": {
+      "content": "                              ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "6": {
+      "content": "                             ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "7": {
+      "content": "                            ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "8": {
+      "content": "                           ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "9": {
+      "content": "                          ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "10": {
+      "content": "                         ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "11": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "12": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "13": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "14": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "15": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "16": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "17": {
+      "content": "                  ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "18": {
+      "content": "                 ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "19": {
+      "content": "                ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "20": {
+      "content": "               ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "21": {
+      "content": "              ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "22": {
+      "content": "             ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "23": {
+      "content": "            ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "24": {
+      "content": "           ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "25": {
+      "content": "          ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "26": {
+      "content": "         ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "27": {
+      "content": "        ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "28": {
+      "content": "       ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "29": {
+      "content": "      ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "30": {
+      "content": "     ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "31": {
+      "content": "    ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "32": {
+      "content": "   ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "33": {
+      "content": "  ",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "34": {
+      "content": "\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "35": {
+      "content": "\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "36": {
+      "content": "\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "37": {
+      "content": "\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "38": {
+      "content": "\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "39": {
+      "content": "\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "40": {
+      "content": "\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "41": {
+      "content": "\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "42": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "43": {
+      "content": "<|c|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "44": {
+      "content": "<|c++|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "45": {
+      "content": "<|python|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "46": {
+      "content": "<|javascript|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "47": {
+      "content": "<|markdown|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "48": {
+      "content": "<|html|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49": {
+      "content": "<|css|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50": {
+      "content": "<|vue|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "51": {
+      "content": "<|java|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "52": {
+      "content": "PI:URL",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "53": {
+      "content": "PI:EMAIL",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "54": {
+      "content": "PI:ACCOUNT_NUM",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "55": {
+      "content": "PI:PHONE_NUM",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "56": {
+      "content": "PI:BUSINESS_NUM",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "57": {
+      "content": "PI:ANNON",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "58": {
+      "content": "PI:KEY",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "59": {
+      "content": "PI:ID",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "60": {
+      "content": "PI:IP_ADDRESS",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "61": {
+      "content": "PI:USER",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "62": {
+      "content": "[unused0]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "63": {
+      "content": "[unused1]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "64": {
+      "content": "[unused2]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65": {
+      "content": "[unused3]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "66": {
+      "content": "[unused4]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "67": {
+      "content": "[unused5]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "68": {
+      "content": "[unused6]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "69": {
+      "content": "[unused7]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "70": {
+      "content": "[unused8]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "71": {
+      "content": "[unused9]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "72": {
+      "content": "[unused10]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73": {
+      "content": "[unused11]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "74": {
+      "content": "[unused12]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "75": {
+      "content": "[unused13]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "76": {
+      "content": "[unused14]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "77": {
+      "content": "[unused15]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "78": {
+      "content": "[unused16]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "79": {
+      "content": "[unused17]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "80": {
+      "content": "[unused18]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "81": {
+      "content": "[unused19]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "82": {
+      "content": "[unused20]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "83": {
+      "content": "[unused21]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "84": {
+      "content": "[unused22]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "85": {
+      "content": "[unused23]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "86": {
+      "content": "[unused24]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "87": {
+      "content": "[unused25]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "88": {
+      "content": "[unused26]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "89": {
+      "content": "[unused27]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "90": {
+      "content": "[unused28]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "91": {
+      "content": "[unused29]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92": {
+      "content": "[unused30]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "93": {
+      "content": "[unused31]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "94": {
+      "content": "[unused32]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "95": {
+      "content": "[unused33]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "96": {
+      "content": "[unused34]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "97": {
+      "content": "[unused35]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "98": {
+      "content": "[unused36]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "99": {
+      "content": "[unused37]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[unused38]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[unused39]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[unused40]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[unused41]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[unused42]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "105": {
+      "content": "[unused43]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "106": {
+      "content": "[unused44]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "107": {
+      "content": "[unused45]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "108": {
+      "content": "[unused46]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "109": {
+      "content": "[unused47]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "110": {
+      "content": "[unused48]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "111": {
+      "content": "[unused49]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "112": {
+      "content": "[unused50]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "113": {
+      "content": "[unused51]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "114": {
+      "content": "[unused52]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "115": {
+      "content": "[unused53]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "116": {
+      "content": "[unused54]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "117": {
+      "content": "[unused55]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "118": {
+      "content": "[unused56]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "119": {
+      "content": "[unused57]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "120": {
+      "content": "[unused58]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "121": {
+      "content": "[unused59]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "122": {
+      "content": "[unused60]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "123": {
+      "content": "[unused61]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "124": {
+      "content": "[unused62]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "125": {
+      "content": "[unused63]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "126": {
+      "content": "[unused64]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "127": {
+      "content": "[unused65]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128": {
+      "content": "[unused66]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "129": {
+      "content": "[unused67]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "130": {
+      "content": "[unused68]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "131": {
+      "content": "[unused69]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "132": {
+      "content": "[unused70]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "133": {
+      "content": "[unused71]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "134": {
+      "content": "[unused72]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "135": {
+      "content": "[unused73]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "136": {
+      "content": "[unused74]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "137": {
+      "content": "[unused75]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "138": {
+      "content": "[unused76]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "139": {
+      "content": "[unused77]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "140": {
+      "content": "[unused78]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "141": {
+      "content": "[unused79]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "142": {
+      "content": "[unused80]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "143": {
+      "content": "[unused81]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "144": {
+      "content": "[unused82]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "145": {
+      "content": "[unused83]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "146": {
+      "content": "[unused84]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "147": {
+      "content": "[unused85]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "148": {
+      "content": "[unused86]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "149": {
+      "content": "[unused87]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "150": {
+      "content": "[unused88]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151": {
+      "content": "[unused89]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "152": {
+      "content": "[unused90]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "153": {
+      "content": "[unused91]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "154": {
+      "content": "[unused92]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "155": {
+      "content": "[unused93]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "156": {
+      "content": "[unused94]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "157": {
+      "content": "[unused95]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "158": {
+      "content": "[unused96]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "159": {
+      "content": "[unused97]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "160": {
+      "content": "[unused98]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "161": {
+      "content": "[unused99]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "162": {
+      "content": "[extra_id_0]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "163": {
+      "content": "[extra_id_1]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "164": {
+      "content": "[extra_id_2]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "165": {
+      "content": "[extra_id_3]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "166": {
+      "content": "[extra_id_4]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "167": {
+      "content": "[extra_id_5]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "168": {
+      "content": "[extra_id_6]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "169": {
+      "content": "[extra_id_7]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "170": {
+      "content": "[extra_id_8]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "171": {
+      "content": "[extra_id_9]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "172": {
+      "content": "[extra_id_10]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "173": {
+      "content": "[extra_id_11]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "174": {
+      "content": "[extra_id_12]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "175": {
+      "content": "[extra_id_13]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "176": {
+      "content": "[extra_id_14]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "177": {
+      "content": "[extra_id_15]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "178": {
+      "content": "[extra_id_16]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "179": {
+      "content": "[extra_id_17]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "180": {
+      "content": "[extra_id_18]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "181": {
+      "content": "[extra_id_19]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "182": {
+      "content": "[extra_id_20]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "183": {
+      "content": "[extra_id_21]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "184": {
+      "content": "[extra_id_22]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "185": {
+      "content": "[extra_id_23]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "186": {
+      "content": "[extra_id_24]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "187": {
+      "content": "[extra_id_25]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "188": {
+      "content": "[extra_id_26]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "189": {
+      "content": "[extra_id_27]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "190": {
+      "content": "[extra_id_28]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "191": {
+      "content": "[extra_id_29]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "192": {
+      "content": "[extra_id_30]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "193": {
+      "content": "[extra_id_31]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "194": {
+      "content": "[extra_id_32]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "195": {
+      "content": "[extra_id_33]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "196": {
+      "content": "[extra_id_34]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "197": {
+      "content": "[extra_id_35]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "198": {
+      "content": "[extra_id_36]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "199": {
+      "content": "[extra_id_37]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "200": {
+      "content": "[extra_id_38]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "201": {
+      "content": "[extra_id_39]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "202": {
+      "content": "[extra_id_40]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "203": {
+      "content": "[extra_id_41]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "204": {
+      "content": "[extra_id_42]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "205": {
+      "content": "[extra_id_43]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "206": {
+      "content": "[extra_id_44]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "207": {
+      "content": "[extra_id_45]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "208": {
+      "content": "[extra_id_46]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "209": {
+      "content": "[extra_id_47]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "210": {
+      "content": "[extra_id_48]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "211": {
+      "content": "[extra_id_49]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "212": {
+      "content": "[extra_id_50]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "213": {
+      "content": "[extra_id_51]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "214": {
+      "content": "[extra_id_52]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "215": {
+      "content": "[extra_id_53]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "216": {
+      "content": "[extra_id_54]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "217": {
+      "content": "[extra_id_55]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "218": {
+      "content": "[extra_id_56]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "219": {
+      "content": "[extra_id_57]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "220": {
+      "content": "[extra_id_58]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "221": {
+      "content": "[extra_id_59]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "222": {
+      "content": "[extra_id_60]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "223": {
+      "content": "[extra_id_61]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "224": {
+      "content": "[extra_id_62]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "225": {
+      "content": "[extra_id_63]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "226": {
+      "content": "[extra_id_64]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "227": {
+      "content": "[extra_id_65]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "228": {
+      "content": "[extra_id_66]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "229": {
+      "content": "[extra_id_67]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "230": {
+      "content": "[extra_id_68]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "231": {
+      "content": "[extra_id_69]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "232": {
+      "content": "[extra_id_70]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "233": {
+      "content": "[extra_id_71]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "234": {
+      "content": "[extra_id_72]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "235": {
+      "content": "[extra_id_73]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "236": {
+      "content": "[extra_id_74]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "237": {
+      "content": "[extra_id_75]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "238": {
+      "content": "[extra_id_76]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "239": {
+      "content": "[extra_id_77]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "240": {
+      "content": "[extra_id_78]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "241": {
+      "content": "[extra_id_79]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "242": {
+      "content": "[extra_id_80]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "243": {
+      "content": "[extra_id_81]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "244": {
+      "content": "[extra_id_82]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "245": {
+      "content": "[extra_id_83]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "246": {
+      "content": "[extra_id_84]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "247": {
+      "content": "[extra_id_85]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "248": {
+      "content": "[extra_id_86]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "249": {
+      "content": "[extra_id_87]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250": {
+      "content": "[extra_id_88]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "251": {
+      "content": "[extra_id_89]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "252": {
+      "content": "[extra_id_90]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "253": {
+      "content": "[extra_id_91]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "254": {
+      "content": "[extra_id_92]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "255": {
+      "content": "[extra_id_93]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "256": {
+      "content": "[extra_id_94]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "257": {
+      "content": "[extra_id_95]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "258": {
+      "content": "[extra_id_96]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "259": {
+      "content": "[extra_id_97]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "260": {
+      "content": "[extra_id_98]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "261": {
+      "content": "[extra_id_99]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "262": {
+      "content": "[extra_id_100]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "263": {
+      "content": "[extra_id_101]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "264": {
+      "content": "[extra_id_102]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "265": {
+      "content": "[extra_id_103]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "266": {
+      "content": "[extra_id_104]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "267": {
+      "content": "[extra_id_105]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "268": {
+      "content": "[extra_id_106]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "269": {
+      "content": "[extra_id_107]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "270": {
+      "content": "[extra_id_108]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "271": {
+      "content": "[extra_id_109]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "272": {
+      "content": "[extra_id_110]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "273": {
+      "content": "[extra_id_111]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "274": {
+      "content": "[extra_id_112]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "275": {
+      "content": "[extra_id_113]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "276": {
+      "content": "[extra_id_114]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "277": {
+      "content": "[extra_id_115]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "278": {
+      "content": "[extra_id_116]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "279": {
+      "content": "[extra_id_117]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "280": {
+      "content": "[extra_id_118]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "281": {
+      "content": "[extra_id_119]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "282": {
+      "content": "[extra_id_120]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "283": {
+      "content": "[extra_id_121]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "284": {
+      "content": "[extra_id_122]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "285": {
+      "content": "[extra_id_123]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "286": {
+      "content": "[extra_id_124]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "287": {
+      "content": "[extra_id_125]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "288": {
+      "content": "[extra_id_126]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "289": {
+      "content": "[extra_id_127]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "290": {
+      "content": "[extra_id_128]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "291": {
+      "content": "[extra_id_129]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "292": {
+      "content": "[extra_id_130]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "293": {
+      "content": "[extra_id_131]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "294": {
+      "content": "[extra_id_132]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "295": {
+      "content": "[extra_id_133]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "296": {
+      "content": "[extra_id_134]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "297": {
+      "content": "[extra_id_135]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "298": {
+      "content": "[extra_id_136]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "299": {
+      "content": "[extra_id_137]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "300": {
+      "content": "[extra_id_138]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "301": {
+      "content": "[extra_id_139]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "302": {
+      "content": "[extra_id_140]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "303": {
+      "content": "[extra_id_141]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "304": {
+      "content": "[extra_id_142]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "305": {
+      "content": "[extra_id_143]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "306": {
+      "content": "[extra_id_144]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "307": {
+      "content": "[extra_id_145]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "308": {
+      "content": "[extra_id_146]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "309": {
+      "content": "[extra_id_147]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "310": {
+      "content": "[extra_id_148]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "311": {
+      "content": "[extra_id_149]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "312": {
+      "content": "[extra_id_150]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "313": {
+      "content": "[extra_id_151]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "314": {
+      "content": "[extra_id_152]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "315": {
+      "content": "[extra_id_153]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "316": {
+      "content": "[extra_id_154]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "317": {
+      "content": "[extra_id_155]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "318": {
+      "content": "[extra_id_156]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "319": {
+      "content": "[extra_id_157]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "320": {
+      "content": "[extra_id_158]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "321": {
+      "content": "[extra_id_159]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "322": {
+      "content": "[extra_id_160]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "323": {
+      "content": "[extra_id_161]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "324": {
+      "content": "[extra_id_162]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "325": {
+      "content": "[extra_id_163]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "326": {
+      "content": "[extra_id_164]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "327": {
+      "content": "[extra_id_165]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "328": {
+      "content": "[extra_id_166]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "329": {
+      "content": "[extra_id_167]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "330": {
+      "content": "[extra_id_168]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "331": {
+      "content": "[extra_id_169]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "332": {
+      "content": "[extra_id_170]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "333": {
+      "content": "[extra_id_171]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "334": {
+      "content": "[extra_id_172]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "335": {
+      "content": "[extra_id_173]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "336": {
+      "content": "[extra_id_174]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "337": {
+      "content": "[extra_id_175]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "338": {
+      "content": "[extra_id_176]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "339": {
+      "content": "[extra_id_177]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "340": {
+      "content": "[extra_id_178]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "341": {
+      "content": "[extra_id_179]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "342": {
+      "content": "[extra_id_180]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "343": {
+      "content": "[extra_id_181]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "344": {
+      "content": "[extra_id_182]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "345": {
+      "content": "[extra_id_183]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "346": {
+      "content": "[extra_id_184]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "347": {
+      "content": "[extra_id_185]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "348": {
+      "content": "[extra_id_186]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "349": {
+      "content": "[extra_id_187]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "350": {
+      "content": "[extra_id_188]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "351": {
+      "content": "[extra_id_189]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "352": {
+      "content": "[extra_id_190]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "353": {
+      "content": "[extra_id_191]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "354": {
+      "content": "[extra_id_192]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "355": {
+      "content": "[extra_id_193]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "356": {
+      "content": "[extra_id_194]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "357": {
+      "content": "[extra_id_195]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "358": {
+      "content": "[extra_id_196]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "359": {
+      "content": "[extra_id_197]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "360": {
+      "content": "[extra_id_198]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "361": {
+      "content": "[|endofturn|]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_token": [
+    "[unused0]",
+    "[unused1]",
+    "[unused2]",
+    "[unused3]",
+    "[unused4]",
+    "[unused5]",
+    "[unused6]",
+    "[unused7]",
+    "[unused8]",
+    "[unused9]",
+    "[unused10]",
+    "[unused11]",
+    "[unused12]",
+    "[unused13]",
+    "[unused14]",
+    "[unused15]",
+    "[unused16]",
+    "[unused17]",
+    "[unused18]",
+    "[unused19]",
+    "[unused20]",
+    "[unused21]",
+    "[unused22]",
+    "[unused23]",
+    "[unused24]",
+    "[unused25]",
+    "[unused26]",
+    "[unused27]",
+    "[unused28]",
+    "[unused29]",
+    "[unused30]",
+    "[unused31]",
+    "[unused32]",
+    "[unused33]",
+    "[unused34]",
+    "[unused35]",
+    "[unused36]",
+    "[unused37]",
+    "[unused38]",
+    "[unused39]",
+    "[unused40]",
+    "[unused41]",
+    "[unused42]",
+    "[unused43]",
+    "[unused44]",
+    "[unused45]",
+    "[unused46]",
+    "[unused47]",
+    "[unused48]",
+    "[unused49]",
+    "[unused50]",
+    "[unused51]",
+    "[unused52]",
+    "[unused53]",
+    "[unused54]",
+    "[unused55]",
+    "[unused56]",
+    "[unused57]",
+    "[unused58]",
+    "[unused59]",
+    "[unused60]",
+    "[unused61]",
+    "[unused62]",
+    "[unused63]",
+    "[unused64]",
+    "[unused65]",
+    "[unused66]",
+    "[unused67]",
+    "[unused68]",
+    "[unused69]",
+    "[unused70]",
+    "[unused71]",
+    "[unused72]",
+    "[unused73]",
+    "[unused74]",
+    "[unused75]",
+    "[unused76]",
+    "[unused77]",
+    "[unused78]",
+    "[unused79]",
+    "[unused80]",
+    "[unused81]",
+    "[unused82]",
+    "[unused83]",
+    "[unused84]",
+    "[unused85]",
+    "[unused86]",
+    "[unused87]",
+    "[unused88]",
+    "[unused89]",
+    "[unused90]",
+    "[unused91]",
+    "[unused92]",
+    "[unused93]",
+    "[unused94]",
+    "[unused95]",
+    "[unused96]",
+    "[unused97]",
+    "[unused98]",
+    "[unused99]",
+    "[extra_id_0]",
+    "[extra_id_1]",
+    "[extra_id_2]",
+    "[extra_id_3]",
+    "[extra_id_4]",
+    "[extra_id_5]",
+    "[extra_id_6]",
+    "[extra_id_7]",
+    "[extra_id_8]",
+    "[extra_id_9]",
+    "[extra_id_10]",
+    "[extra_id_11]",
+    "[extra_id_12]",
+    "[extra_id_13]",
+    "[extra_id_14]",
+    "[extra_id_15]",
+    "[extra_id_16]",
+    "[extra_id_17]",
+    "[extra_id_18]",
+    "[extra_id_19]",
+    "[extra_id_20]",
+    "[extra_id_21]",
+    "[extra_id_22]",
+    "[extra_id_23]",
+    "[extra_id_24]",
+    "[extra_id_25]",
+    "[extra_id_26]",
+    "[extra_id_27]",
+    "[extra_id_28]",
+    "[extra_id_29]",
+    "[extra_id_30]",
+    "[extra_id_31]",
+    "[extra_id_32]",
+    "[extra_id_33]",
+    "[extra_id_34]",
+    "[extra_id_35]",
+    "[extra_id_36]",
+    "[extra_id_37]",
+    "[extra_id_38]",
+    "[extra_id_39]",
+    "[extra_id_40]",
+    "[extra_id_41]",
+    "[extra_id_42]",
+    "[extra_id_43]",
+    "[extra_id_44]",
+    "[extra_id_45]",
+    "[extra_id_46]",
+    "[extra_id_47]",
+    "[extra_id_48]",
+    "[extra_id_49]",
+    "[extra_id_50]",
+    "[extra_id_51]",
+    "[extra_id_52]",
+    "[extra_id_53]",
+    "[extra_id_54]",
+    "[extra_id_55]",
+    "[extra_id_56]",
+    "[extra_id_57]",
+    "[extra_id_58]",
+    "[extra_id_59]",
+    "[extra_id_60]",
+    "[extra_id_61]",
+    "[extra_id_62]",
+    "[extra_id_63]",
+    "[extra_id_64]",
+    "[extra_id_65]",
+    "[extra_id_66]",
+    "[extra_id_67]",
+    "[extra_id_68]",
+    "[extra_id_69]",
+    "[extra_id_70]",
+    "[extra_id_71]",
+    "[extra_id_72]",
+    "[extra_id_73]",
+    "[extra_id_74]",
+    "[extra_id_75]",
+    "[extra_id_76]",
+    "[extra_id_77]",
+    "[extra_id_78]",
+    "[extra_id_79]",
+    "[extra_id_80]",
+    "[extra_id_81]",
+    "[extra_id_82]",
+    "[extra_id_83]",
+    "[extra_id_84]",
+    "[extra_id_85]",
+    "[extra_id_86]",
+    "[extra_id_87]",
+    "[extra_id_88]",
+    "[extra_id_89]",
+    "[extra_id_90]",
+    "[extra_id_91]",
+    "[extra_id_92]",
+    "[extra_id_93]",
+    "[extra_id_94]",
+    "[extra_id_95]",
+    "[extra_id_96]",
+    "[extra_id_97]",
+    "[extra_id_98]",
+    "[extra_id_99]",
+    "[extra_id_100]",
+    "[extra_id_101]",
+    "[extra_id_102]",
+    "[extra_id_103]",
+    "[extra_id_104]",
+    "[extra_id_105]",
+    "[extra_id_106]",
+    "[extra_id_107]",
+    "[extra_id_108]",
+    "[extra_id_109]",
+    "[extra_id_110]",
+    "[extra_id_111]",
+    "[extra_id_112]",
+    "[extra_id_113]",
+    "[extra_id_114]",
+    "[extra_id_115]",
+    "[extra_id_116]",
+    "[extra_id_117]",
+    "[extra_id_118]",
+    "[extra_id_119]",
+    "[extra_id_120]",
+    "[extra_id_121]",
+    "[extra_id_122]",
+    "[extra_id_123]",
+    "[extra_id_124]",
+    "[extra_id_125]",
+    "[extra_id_126]",
+    "[extra_id_127]",
+    "[extra_id_128]",
+    "[extra_id_129]",
+    "[extra_id_130]",
+    "[extra_id_131]",
+    "[extra_id_132]",
+    "[extra_id_133]",
+    "[extra_id_134]",
+    "[extra_id_135]",
+    "[extra_id_136]",
+    "[extra_id_137]",
+    "[extra_id_138]",
+    "[extra_id_139]",
+    "[extra_id_140]",
+    "[extra_id_141]",
+    "[extra_id_142]",
+    "[extra_id_143]",
+    "[extra_id_144]",
+    "[extra_id_145]",
+    "[extra_id_146]",
+    "[extra_id_147]",
+    "[extra_id_148]",
+    "[extra_id_149]",
+    "[extra_id_150]",
+    "[extra_id_151]",
+    "[extra_id_152]",
+    "[extra_id_153]",
+    "[extra_id_154]",
+    "[extra_id_155]",
+    "[extra_id_156]",
+    "[extra_id_157]",
+    "[extra_id_158]",
+    "[extra_id_159]",
+    "[extra_id_160]",
+    "[extra_id_161]",
+    "[extra_id_162]",
+    "[extra_id_163]",
+    "[extra_id_164]",
+    "[extra_id_165]",
+    "[extra_id_166]",
+    "[extra_id_167]",
+    "[extra_id_168]",
+    "[extra_id_169]",
+    "[extra_id_170]",
+    "[extra_id_171]",
+    "[extra_id_172]",
+    "[extra_id_173]",
+    "[extra_id_174]",
+    "[extra_id_175]",
+    "[extra_id_176]",
+    "[extra_id_177]",
+    "[extra_id_178]",
+    "[extra_id_179]",
+    "[extra_id_180]",
+    "[extra_id_181]",
+    "[extra_id_182]",
+    "[extra_id_183]",
+    "[extra_id_184]",
+    "[extra_id_185]",
+    "[extra_id_186]",
+    "[extra_id_187]",
+    "[extra_id_188]",
+    "[extra_id_189]",
+    "[extra_id_190]",
+    "[extra_id_191]",
+    "[extra_id_192]",
+    "[extra_id_193]",
+    "[extra_id_194]",
+    "[extra_id_195]",
+    "[extra_id_196]",
+    "[extra_id_197]",
+    "[extra_id_198]",
+    "[|endofturn|]",
+    "PI:URL",
+    "PI:EMAIL",
+    "PI:ACCOUNT_NUM",
+    "PI:PHONE_NUM",
+    "PI:BUSINESS_NUM",
+    "PI:ANNON",
+    "PI:KEY",
+    "PI:ID",
+    "PI:IP_ADDRESS",
+    "PI:USER"
+  ],
+  "bos_token": "[BOS]",
+  "chat_template": "{% for message in messages %}{% if loop.first and message['role'] != 'system' %}{{ '[|system|][|endofturn|]\\n' }}{% endif %}{% set content = message['content'] %}{% if '</thought>' in content %}{% set content = content.split('</thought>')[-1].lstrip('\\n') %}{% endif %}{{ '[|' + message['role'] + '|]' + content }}{% if not message['role'] == 'user' %}{{ '[|endofturn|]' }}{% endif %}{% if not loop.last %}{{ '\\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '\\n[|assistant|]<thought>\\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "[|endofturn|]",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "[UNK]"
+}