src/transformers/models/xglm/configuration_xglm.py/XGLMConfig/__init__
class XGLMConfig:    def __init__(
        self,
        vocab_size=256008,
        max_position_embeddings=2048,
        d_model=1024,
        ffn_dim=4096,
        num_layers=24,
        attention_heads=16,
        activation_function="gelu",
        dropout=0.1,
        attention_dropout=0.1,
        activation_dropout=0.0,
        layerdrop=0.0,
        init_std=0.02,
        scale_embedding=True,
        use_cache=True,
        decoder_start_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.ffn_dim = ffn_dim
        self.num_layers = num_layers
        self.attention_heads = attention_heads
        self.activation_function = activation_function
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.layerdrop = layerdrop
        self.init_std = init_std
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        self.use_cache = use_cache

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )