| """Configuration for the Transformers-compatible pinyin-code causal LM.""" | |
| from __future__ import annotations | |
| from transformers import PretrainedConfig | |
| class PinyinCodeConfig(PretrainedConfig): | |
| """Configuration for the compact GPT-style pinyin-code decoder.""" | |
| model_type = "pinyin_code" | |
| def __init__( | |
| self, | |
| vocab_size: int = 8000, | |
| block_size: int = 128, | |
| n_layer: int = 6, | |
| n_head: int = 8, | |
| n_embd: int = 256, | |
| dropout: float = 0.1, | |
| bos_token_id: int | None = None, | |
| eos_token_id: int | None = None, | |
| pad_token_id: int | None = None, | |
| unk_token_id: int | None = None, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__( | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| pad_token_id=pad_token_id, | |
| unk_token_id=unk_token_id, | |
| **kwargs, | |
| ) | |
| self.vocab_size = vocab_size | |
| self.block_size = block_size | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_embd = n_embd | |
| self.dropout = dropout | |
| self.num_hidden_layers = n_layer | |
| self.num_attention_heads = n_head | |
| self.hidden_size = n_embd | |
| self.max_position_embeddings = block_size | |
| self.is_decoder = True | |
| self.is_encoder_decoder = False | |
| self.use_cache = False | |