`"silu"`):
            The non-linear activation function (function or string) in the decoder.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
            Initialization type for the alphas.
        alphas_initializer_range (`float`, *optional*, defaults to 0.0):
            The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
            Attention.
        alpha_type (`str`, *optional*, defaults to `"float"`):
            Whether the gating alphas should be vectors or single floats.
        rms_norm_eps (`float`, *optional*, defaults to 1e-6):
            The epsilon used by the rms normalization layers.
        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`.
        pad_token_id (`int`, *optional*, defaults to 0)
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1)
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2)
            End of stream token id.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        cross_layer_interval (`int`, *optional*, default to 1)
            Interval for cross attention (from text to image) layers.
        qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
        freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
        freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
            Exceptions to freezing text layers when `freeze_text_layers` is `True`
        freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
        freeze_vision_layers (`bool`, *optional*, defaults to `True`):  Whether to freeze vision layers
        freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
            Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
        use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
        vision_config (`IdeficsVisionConfig`,  *optional*): Custom vision config or dict
        perceiver_config (`IdeficsPerceiverConfig`,  *optional*): Custom perceiver config or dict

    Example:

    ```python
    >>> from transformers import IdeficsModel, IdeficsConfig

    >>> # Initializing a Idefics idefics-9b style configuration
    >>> configuration = IdeficsConfig()

    >>> # Initializing a model from the idefics-9b style configuration
    >>> model = IdeficsModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```r