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EXAONE 4.5

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This model was released on 2026-04-09 and added to Hugging Face Transformers on 2026-05-04.

EXAONE 4.5

Overview

EXAONE 4.5 model is the first open-weight vision language model developed by LG AI Research. Integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, we expand the model’s capability toward multimodality. EXAONE 4.5 features 33 billion parameters in total, including 1.2 billion parameters from the vision encoder. EXAONE 4.5 achieves competitive performance in general benchmark while outperforming SOTA models of similar size in document understanding and Korean contextual reasoning, inheriting powerful language capabilities from our previous language models.

EXAONE 4.5 builds on the foundation of EXAONE 4.0 with several key enhancements. The vocabulary size has been expanded to 153,600, and the context window now supports up to 256K tokens. In addition, a Multi-Token Prediction (MTP) mechanism has been introduced, further improving the model’s performance.

For more details, please refer to the technical report, blog and GitHub.

All model weights including quantized version are available at Huggingface Collections.

Usage tips

To achieve the expected performance, we recommend using the following configurations:

  • We recommend to use temperature=1.0, top_p=0.95, presence_penalty=1.5 for general purpose.
  • We recommend to use temperature=0.6, top_p=0.95, presence_penalty=1.5, top_k=20 for OCR/document-related tasks, and Korean inputs.
  • We recommend to use temperature=1.0, top_p=0.95 for text-only inputs.
  • Different from EXAONE-4.0, EXAONE 4.5 uses enable_thinking=True as default. Thus, you need to set enable_thinking=False when you want to use non-reasoning mode.
  • EXAONE 4.5 prefers using \boxed{} format to answer the question. We recommend using this format with the corresponding format instruction for better parsing accuracy.

For tasks that require accurate results, you can run the EXAONE 4.5 model in reasoning mode, whereas for tasks where latency matters more than accuracy, you can run the EXAONE 4.5 model in non-reasoning mode.

Here is the example code for using EXAONE 4.5 model in reasoning mode:

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image

model_id = "LGAI-EXAONE/EXAONE-4.5-33B"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
)

image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = load_image(image_url)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image_url},
            {"type": "text", "text": "Describe the image."},
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,   # default: True
)
inputs = processor(
    text=[text],
    images=[image],
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=64)
generated_text = processor.batch_decode(
    generated_ids[:, inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)[0]
print(generated_text)

Exaone4_5_Config

class transformers.Exaone4_5_Config

< >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None text_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None vision_config: dict | transformers.configuration_utils.PreTrainedConfig | None = None image_token_id: int = 67 video_token_id: int = 68 tie_word_embeddings: bool = False )

Parameters

  • text_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the text backbone.
  • vision_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The config object or dictionary of the vision backbone.
  • image_token_id (int, optional, defaults to 67) — The image token index used as a placeholder for input images.
  • video_token_id (int, optional, defaults to 68) — The video token index used as a placeholder for input videos.
  • tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.

This is the configuration class to store the configuration of a Exaone4_5_Model. It is used to instantiate a Exaone4 5 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 LGAI-EXAONE/EXAONE-4.5-33B

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Exaone4_5_VisionConfig

class transformers.Exaone4_5_VisionConfig

< >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None depth: int = 32 hidden_size: int = 3584 hidden_act: str = 'silu' intermediate_size: int = 3420 num_heads: int = 16 in_channels: int = 3 patch_size: int | list[int] | tuple[int, int] = 14 spatial_merge_size: int = 2 temporal_patch_size: int | list[int] | tuple[int, int] = 2 tokens_per_second: int = 4 window_size: int = 112 out_hidden_size: int = 3584 fullatt_block_indexes: list[int] | tuple[int, ...] = (7, 15, 23, 31) initializer_range: float = 0.02 num_key_value_heads: int = 8 )

Parameters

  • depth (int, optional, defaults to 32) — Number of Transformer layers in the vision encoder.
  • hidden_size (int, optional, defaults to 3584) — Dimension of the hidden representations.
  • hidden_act (str, optional, defaults to silu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
  • intermediate_size (int, optional, defaults to 3420) — Dimension of the MLP representations.
  • num_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
  • in_channels (int, optional, defaults to 3) — The number of input channels.
  • patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 14) — The size (resolution) of each patch.
  • spatial_merge_size (int, optional, defaults to 2) — The size of the spatial merge window used to reduce the number of visual tokens by merging neighboring patches.
  • temporal_patch_size (Union[int, list[int], tuple[int, int]], optional, defaults to 2) — Temporal patch size used in the 3D patch embedding for video inputs.
  • tokens_per_second (int, optional, defaults to 41) — Number of tokens to merge for each second of video.
  • window_size (int, optional, defaults to 11) — Size of windows.
  • out_hidden_size (int, optional, defaults to 3584) — The output hidden size of the vision model.
  • fullatt_block_indexes (int, optional, defaults to [7, 15, 23, 31]) — Indices of layers with full attention
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • num_key_value_heads (int, optional, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to num_attention_heads.

This is the configuration class to store the configuration of a Exaone4_5_Model. It is used to instantiate a Exaone4 5 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 LGAI-EXAONE/EXAONE-4.5-33B

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Exaone4_5_Processor

class transformers.Exaone4_5_Processor

< >

( image_processor = None tokenizer = None video_processor = None chat_template = None **kwargs )

Parameters

  • image_processor (Qwen2VLImageProcessor) — The image processor is a required input.
  • tokenizer (tokenizer_class) — The tokenizer is a required input.
  • video_processor (Qwen2VLVideoProcessor) — The video processor is a required input.
  • chat_template (str) — A Jinja template to convert lists of messages in a chat into a tokenizable string.

Constructs a Exaone4_5_Processor which wraps a image processor, a tokenizer, and a video processor into a single processor.

Exaone4_5_Processor offers all the functionalities of Qwen2VLImageProcessor, tokenizer_class, and Qwen2VLVideoProcessor. See the ~Qwen2VLImageProcessor, ~tokenizer_class, and ~Qwen2VLVideoProcessor for more information.

post_process_image_text_to_text

< >

( generated_outputs skip_special_tokens = True clean_up_tokenization_spaces = False **kwargs ) → list[str]

Parameters

  • generated_outputs (torch.Tensor or np.ndarray) — The output of the model generate function. The output is expected to be a tensor of shape (batch_size, sequence_length) or (sequence_length,).
  • skip_special_tokens (bool, optional, defaults to True) — Whether or not to remove special tokens in the output. Argument passed to the tokenizer’s batch_decode method.
  • clean_up_tokenization_spaces (bool, optional, defaults to False) — Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer’s batch_decode method.
  • **kwargs — Additional arguments to be passed to the tokenizer’s batch_decode method.

Returns

list[str]

The decoded text.

Post-process the output of the model to decode the text.

Exaone4_5_VisionModel

class transformers.Exaone4_5_VisionModel

< >

( config: Exaone4_5_VisionConfig *inputs **kwargs )

forward

< >

( hidden_states: Tensor grid_thw: Tensor **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → torch.Tensor

Parameters

  • hidden_states (torch.Tensor of shape (seq_len, hidden_size)) — The final hidden states of the model.
  • grid_thw (torch.Tensor of shape (num_images_or_videos, 3)) — The temporal, height and width of feature shape of each image in LLM.

Returns

torch.Tensor

hidden_states.

Exaone4_5_Model

class transformers.Exaone4_5_Model

< >

( config: Exaone4_5_Config )

Parameters

  • config (Exaone4_5_Config) — 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 from_pretrained() method to load the model weights.

The bare Exaone4 5 Model outputting raw hidden-states without any specific head on top.

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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None use_cache: bool | None = None pixel_values: torch.Tensor | None = None pixel_values_videos: torch.FloatTensor | None = None image_grid_thw: torch.LongTensor | None = None video_grid_thw: torch.LongTensor | None = None second_per_grid_ts: torch.Tensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using Qwen2VLImageProcessor. See Qwen2VLImageProcessor.__call__() for details (Exaone4_5_Processor uses Qwen2VLImageProcessor for processing images).
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using Qwen2VLVideoProcessor. See Qwen2VLVideoProcessor.call() for details (Exaone4_5_Processor uses Qwen2VLVideoProcessor for processing videos).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • second_per_grid_ts (torch.Tensor of shape (num_videos), optional) — The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.

Returns

BaseModelOutputWithPast or tuple(torch.FloatTensor)

A BaseModelOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Exaone4_5_Config) and inputs.

The Exaone4_5_Model forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

    If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Exaone4_5_ForConditionalGeneration

class transformers.Exaone4_5_ForConditionalGeneration

< >

( config )

Main EXAONE 4.5 conditional generation class.

Note: Unlike Qwen2VL, the EXAONE 4.5 vision encoder uses 2D rotary positional embeddings (2D-RoPE) and adopts a Grouped Query Attention (GQA) structure throughout the multimodal stack.

forward

< >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None position_ids: torch.LongTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None use_cache: bool | None = None pixel_values: torch.Tensor | None = None pixel_values_videos: torch.FloatTensor | None = None image_grid_thw: torch.LongTensor | None = None video_grid_thw: torch.LongTensor | None = None second_per_grid_ts: torch.Tensor | None = None logits_to_keep: int | torch.Tensor = 0 **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → CausalLMOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
  • 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).
  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using Qwen2VLImageProcessor. See Qwen2VLImageProcessor.__call__() for details (Exaone4_5_Processor uses Qwen2VLImageProcessor for processing images).
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using Qwen2VLVideoProcessor. See Qwen2VLVideoProcessor.call() for details (Exaone4_5_Processor uses Qwen2VLVideoProcessor for processing videos).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • second_per_grid_ts (torch.Tensor of shape (num_videos), optional) — The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
  • logits_to_keep (Union[int, torch.Tensor], optional, defaults to 0) — If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

CausalLMOutputWithPast or tuple(torch.FloatTensor)

A CausalLMOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Exaone4_5_Config) and inputs.

The Exaone4_5_ForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import AutoProcessor, Exaone4_5_ForConditionalGeneration
>>> import torch

>>> model = Exaone4_5_ForConditionalGeneration.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")
>>> processor = AutoProcessor.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")

>>> messages = [
...     {
...         "role": "user",
...         "content": [
...             {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
...             {"type": "text", "text": "Describe the image."},
...         ],
...     }
... ]
>>> inputs = processor.apply_chat_template(
...     messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
... )
>>> inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
>>> generated_ids = model.generate(**inputs, max_new_tokens=64)
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