| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class ProPrimeConfig(PretrainedConfig): | |
| model_type = "proprime" | |
| def __init__( | |
| self, | |
| vocab_size=33, | |
| mask_token_id=32, | |
| pad_token_id=1, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=1026, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| position_embedding_type="rotary", | |
| use_cache=True, | |
| emb_layer_norm_before=None, | |
| token_dropout=False, | |
| flash_attention=True, | |
| structure_vocab_size=100, | |
| value_loss_scale=0.01, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| pad_token_id=pad_token_id, mask_token_id=mask_token_id, **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.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.position_embedding_type = position_embedding_type | |
| self.use_cache = use_cache | |
| self.emb_layer_norm_before = emb_layer_norm_before | |
| self.token_dropout = token_dropout | |
| self.flash_attention = flash_attention | |
| self.structure_vocab_size = structure_vocab_size | |
| ProPrimeConfig.register_for_auto_class() |