| from transformers.models.llama import LlamaConfig | |
| class HyperLlamaConfig(LlamaConfig): | |
| model_type = "hyperllama" | |
| def __init__( | |
| self, | |
| vocab_size=32000, | |
| hidden_size=4096, | |
| intermediate_size=11008, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=None, | |
| hidden_act="silu", | |
| max_position_embeddings=2048, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| pad_token_id=None, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| pretraining_tp=1, | |
| tie_word_embeddings=False, | |
| rope_theta=10000.0, | |
| rope_scaling=None, | |
| attention_bias=False, | |
| attention_dropout=0.0, | |
| mlp_bias=False, | |
| head_dim=None, | |
| lm_head_normalization_factor: int = 1, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| vocab_size, | |
| hidden_size, | |
| intermediate_size, | |
| num_hidden_layers, | |
| num_attention_heads, | |
| num_key_value_heads, | |
| hidden_act, | |
| max_position_embeddings, | |
| initializer_range, | |
| rms_norm_eps, | |
| use_cache, | |
| pad_token_id, | |
| bos_token_id, | |
| eos_token_id, | |
| pretraining_tp, | |
| tie_word_embeddings, | |
| rope_theta, | |
| rope_scaling, | |
| attention_bias, | |
| attention_dropout, | |
| mlp_bias, | |
| head_dim, | |
| **kwargs, | |
| ) | |
| self.lm_head_normalization_factor = lm_head_normalization_factor | |