""" MotherConfig — HuggingFace-compatible configuration for MOTHER CORE. This is a thin metadata wrapper. The actual architecture parameters come from mother_core.config.ModelConfig. This class exists so HF tools can read 'what architecture am I loading' without needing to import MOTHER CORE internals. """ from transformers import PretrainedConfig class MotherConfig(PretrainedConfig): model_type = "mother_core" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int = 50258, hidden_size: int = 3072, num_hidden_layers: int = 28, num_attention_heads: int = 24, num_key_value_heads: int = 6, ff_mult: float = 4.0, max_position_embeddings: int = 1024, rope_theta: float = 10000.0, rms_norm_eps: float = 1e-5, tie_word_embeddings: bool = False, bos_token_id: int = 1, eos_token_id: int = 2, pad_token_id: int = 0, # MOTHER CORE sovereign identity metadata model_name: str = "MOTHER CORE", model_version: str = "v11", architect: str = "Christopher Kenna", organisation: str = "MediaStream AI Limited", sovereign_nation: str = "United Kingdom", **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.ff_mult = ff_mult self.max_position_embeddings = max_position_embeddings self.rope_theta = rope_theta self.rms_norm_eps = rms_norm_eps self.model_name = model_name self.model_version = model_version self.architect = architect self.organisation = organisation self.sovereign_nation = sovereign_nation super().__init__( tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs, )