fTh`SrSSKJr SSKJr \R "\5r"SS\5rS/r g)zPixtral model configuration)PretrainedConfig)loggingcJ^\rSrSrSrSrSU4SjjrSrU=r$)PixtralVisionConfiga This is the configuration class to store the configuration of a [`PixtralVisionModel`]. It is used to instantiate an Pixtral vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to the vision encoder used by Pixtral-12B. e.g. [pixtral-hf/pixtral-9b](https://huggingface.co/pixtral-hf/pixtral-9b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 1024): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4096): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of input channels in the input images. image_size (`int`, *optional*, defaults to 1024): Max dimension of the input images. patch_size (`int`, *optional*, defaults to 16): Size of the image patches. hidden_act (`str`, *optional*, defaults to `"gelu"`): Activation function used in the hidden layers. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout probability for the attention layers. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Example: ```python >>> from transformers import PixtralVisionModel, PixtralVisionConfig >>> # Initializing a Pixtral-12B style configuration >>> config = PixtralVisionConfig() >>> # Initializing a model (with randomly initialized weights) from the configuration >>> model = PixtralVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```pixtralc  >[T U]"S0U D6 XlX lX0lX@lXPlXplX`lXl Xl Xl X-Ul Xl g)N)super__init__ hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads num_channels patch_size image_sizeattention_dropout hidden_act rope_thetahead_diminitializer_range)selfr rrrrrrrrrrkwargs __class__s i/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/pixtral/configuration_pixtral.pyr PixtralVisionConfig.__init__Ks] "6"&!2!2#6 ($$!2$$#: !2) rrrr rrrrrrrr) irrr!gelugg@g{Gz?) __name__ __module__ __qualname____firstlineno____doc__ model_typer __static_attributes__ __classcell__)rs@rrrs=/ bJ33rrN) r'configuration_utilsrutilsr get_loggerr#loggerr__all__r rrr0s<"3   H %P3*P3f ! !r