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from typing import Any, Optional, Union
from transformers.configuration_utils import PretrainedConfig
class Step3p5Config(PretrainedConfig):
model_type = "step3p5"
architectures = ["Step3p5ForCausalLM"]
def __init__(
self,
hidden_size: int = 4096,
intermediate_size: int = 11264,
num_attention_heads: int = 64,
num_attention_groups: int = 8,
num_hidden_layers: int = 45,
max_seq_len: int = 128000,
vocab_size: int = 128815,
rms_norm_eps: float = 1e-5,
moe_intermediate_size: int = 1280,
moe_num_experts: int = 288,
moe_top_k: int = 8,
rope_theta: float = 10000,
rope_scaling: Optional[dict[str, Any]] = None,
max_position_embeddings: int = 128000,
share_expert_dims: int = 1280,
head_dim: int = 128,
norm_expert_weight: bool = True,
layer_types: list[str] = None,
sliding_window: Optional[int] = None,
moe_layers_enum: tuple[int] = (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44),
**kwargs,
) -> None:
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.num_attention_groups = num_attention_groups
self.num_hidden_layers = num_hidden_layers
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.rms_norm_eps = rms_norm_eps
self.moe_intermediate_size = moe_intermediate_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.max_position_embeddings = max_position_embeddings
self.share_expert_dim = share_expert_dims
self.head_dim = head_dim
self.norm_expert_weight = norm_expert_weight
self.moe_layers_enum = moe_layers_enum
self.layer_types = layer_types
self.sliding_window = sliding_window
super().__init__(**kwargs)
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