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| from __future__ import annotations | |
| from transformers import PretrainedConfig | |
| class RiemannConfig(PretrainedConfig): | |
| model_type = "riemann_openpeer" | |
| def __init__( | |
| self, | |
| vocab_size: int = 50257, | |
| hidden_size: int = 768, | |
| num_hidden_layers: int = 12, | |
| num_attention_heads: int = 12, | |
| intermediate_size: int = 3072, | |
| max_position_embeddings: int = 1024, | |
| layer_norm_eps: float = 1e-05, | |
| hidden_dropout: float = 0.1, | |
| attention_dropout: float = 0.1, | |
| use_gradient_checkpointing: bool = False, | |
| **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.intermediate_size = intermediate_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.layer_norm_eps = layer_norm_eps | |
| self.hidden_dropout = hidden_dropout | |
| self.attention_dropout = attention_dropout | |
| self.use_gradient_checkpointing = use_gradient_checkpointing | |
| kwargs.setdefault("tie_word_embeddings", False) | |
| super().__init__(**kwargs) | |