| from transformers import PretrainedConfig |
|
|
|
|
| class MiniLlmConfig(PretrainedConfig): |
| model_type = "mini_llm" |
|
|
| def __init__( |
| self, |
| vocab_size=16384, |
| hidden_size=512, |
| num_hidden_layers=8, |
| num_attention_heads=8, |
| intermediate_size=1408, |
| max_position_embeddings=512, |
| rms_norm_eps=1e-5, |
| rope_theta=10000.0, |
| tie_word_embeddings=True, |
| **kwargs, |
| ): |
| super().__init__( |
| tie_word_embeddings=tie_word_embeddings, |
| eos_token_id=kwargs.pop("eos_token_id", 0), |
| pad_token_id=kwargs.pop("pad_token_id", 0), |
| **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.max_position_embeddings = max_position_embeddings |
| self.rms_norm_eps = rms_norm_eps |
| self.rope_theta = rope_theta |
|
|
| @classmethod |
| def from_cfg(cls, cfg, **kwargs): |
| return cls( |
| vocab_size=cfg.vocab, |
| hidden_size=cfg.dim, |
| num_hidden_layers=cfg.layers, |
| num_attention_heads=cfg.heads, |
| intermediate_size=cfg.ffn, |
| max_position_embeddings=cfg.ctx, |
| **kwargs, |
| ) |
|
|
