| from transformers import PretrainedConfig | |
| class ParchmentConfig(PretrainedConfig): | |
| model_type = "parchment" | |
| def __init__( | |
| self, | |
| vocab_size: int = 100277, | |
| d_model: int = 768, | |
| n_heads: int = 12, | |
| n_layers: int = 12, | |
| max_seq_len: int = 1024, | |
| rms_norm_eps: float = 1e-6, | |
| rope_base: float = 10000.0, | |
| tie_word_embeddings: bool = True, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.d_model = d_model | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.max_seq_len = max_seq_len | |
| self.rms_norm_eps = rms_norm_eps | |
| self.rope_base = rope_base | |
| # aliases expected by transformers internals | |
| self.num_hidden_layers = n_layers | |
| self.hidden_size = d_model | |
| self.num_attention_heads = n_heads | |
| super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) | |