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                  e«      Z G d„ de«      Zy)zXGLM model configurationé   )ÚPretrainedConfig)Úloggingc                   ó^   ‡ — e Zd ZdZdZdgZddddœZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d	ˆ fd„	Zˆ xZS )
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XGLMConfiga0  
    This is the configuration class to store the configuration of a [`XGLMModel`]. It is used to instantiate an XGLM
    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 XGLM
    [facebook/xglm-564M](https://huggingface.co/facebook/xglm-564M) architecture.

    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 256008):
            Vocabulary size of the XGLM model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`XGLMModel`] or [`FlaxXGLMModel`].
        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).
        d_model (`int`, *optional*, defaults to 1024):
            Dimension of the layers and the pooler layer.
        ffn_dim (`int`, *optional*, defaults to 4096):
            Dimension of the "intermediate" (often named feed-forward) layer in decoder.
        num_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers Transformer decoder.
        attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, dencoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        scale_embedding (`bool`, *optional*, defaults to `True`):
            Scale embeddings by diving by sqrt(d_model).
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).

    Example:

    ```python
    >>> from transformers import XGLMModel, XGLMConfig

    >>> # Initializing a XGLM facebook/xglm-564M style configuration
    >>> configuration = XGLMConfig()

    >>> # Initializing a model from the facebook/xglm-564M style configuration
    >>> model = XGLMModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```ÚxglmÚpast_key_valuesÚattention_headsÚd_modelÚ
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