| import math |
| from transformers import PretrainedConfig |
|
|
| class NeoLMConfig(PretrainedConfig): |
| model_type = "neolm" |
|
|
| def __init__( |
| self, |
| vocab_size: int = 32000, |
| max_position_embeddings: int = 512, |
| hidden_size: int = 512, |
| num_attention_heads: int = 8, |
| num_key_value_heads: int | None = None, |
| num_hidden_layers: int = 8, |
| intermediate_size: int = None, |
| dropout: float = 0.0, |
| tie_word_embeddings: bool = True, |
| rope_base: int = 10_000, |
| mlp_multiple_of: int = 256, |
| rms_norm_eps: float = 1e-6, |
| qk_norm: bool = True, |
| scale_residual_init: bool = True, |
| use_cache: bool = False, |
| pad_token_id: int = None, |
| bos_token_id: int = None, |
| eos_token_id: int = None, |
| **kwargs, |
| ): |
| super().__init__( |
| pad_token_id=pad_token_id, |
| bos_token_id=bos_token_id, |
| eos_token_id=eos_token_id, |
| **kwargs, |
| ) |
|
|
| assert hidden_size % num_attention_heads == 0, ( |
| f"hidden_size ({hidden_size}) يجب أن يقسم على num_attention_heads ({num_attention_heads})" |
| ) |
|
|
| if num_key_value_heads is None: |
| num_key_value_heads = num_attention_heads |
|
|
| if not ( |
| 1 <= num_key_value_heads <= num_attention_heads |
| and num_attention_heads % num_key_value_heads == 0 |
| ): |
| raise ValueError( |
| "num_key_value_heads must be positive " |
| "and divide num_attention_heads" |
| ) |
|
|
| if intermediate_size is None: |
| intermediate_size = mlp_multiple_of * math.ceil( |
| int(8 * hidden_size / 3) / mlp_multiple_of |
| ) |
|
|
| self.vocab_size = vocab_size |
| self.max_position_embeddings = max_position_embeddings |
| self.hidden_size = hidden_size |
| self.num_attention_heads = num_attention_heads |
| self.num_key_value_heads = num_key_value_heads |
| self.num_hidden_layers = num_hidden_layers |
| self.intermediate_size = intermediate_size |
| self.dropout = dropout |
| self.tie_word_embeddings = tie_word_embeddings |
| self.rope_base = rope_base |
| self.mlp_multiple_of = mlp_multiple_of |
| self.rms_norm_eps = rms_norm_eps |
| self.qk_norm = qk_norm |
| self.scale_residual_init = scale_residual_init |
| self.use_cache = use_cache |
|
|
