| 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() |
