|
|
| from __future__ import annotations
|
|
|
| from typing import Any, Optional
|
| from transformers import PretrainedConfig
|
|
|
|
|
| class EveConfig(PretrainedConfig):
|
| model_type = "eve_moe"
|
| attribute_map = {
|
| "num_hidden_layers": "n_layer",
|
| "num_attention_heads": "n_head",
|
| "hidden_size": "n_embd",
|
| "max_position_embeddings": "block_size",
|
| }
|
|
|
| def __init__(
|
| self,
|
| vocab_size: int = 50304,
|
| n_layer: int = 12,
|
| n_embd: int = 512,
|
| n_head: int = 8,
|
| head_dim: int = 64,
|
| block_size: int = 2048,
|
| num_experts: int = 8,
|
| top_k: int = 2,
|
| expert_intermediate_size: int = 1408,
|
| shared_expert_intermediate_size: int = 1408,
|
| router_aux_loss_coef: float = 0.01,
|
| use_checkpointing: bool = False,
|
| rope_theta: float = 10000.0,
|
| **kwargs: Any,
|
| ):
|
| self.vocab_size = vocab_size
|
| self.n_layer = n_layer
|
| self.n_embd = n_embd
|
| self.n_head = n_head
|
| self.head_dim = head_dim
|
| self.block_size = block_size
|
| self.num_experts = num_experts
|
| self.top_k = top_k
|
| self.expert_intermediate_size = expert_intermediate_size
|
| self.shared_expert_intermediate_size = shared_expert_intermediate_size
|
| self.router_aux_loss_coef = router_aux_loss_coef
|
| self.use_checkpointing = use_checkpointing
|
| self.rope_theta = rope_theta
|
| super().__init__(**kwargs)
|
|
|
|
|
| __all__ = ["EveConfig"]
|
|
|
