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84fac55 | 1 2 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 45 46 | """Erk-Linear yapilandirmasi — %20-lineer hibrit (8/40 dikkat katmani Gated DeltaNet)."""
from transformers import PretrainedConfig
class ErkLinearConfig(PretrainedConfig):
"""Hibridin kendisi bir govde tasimaz; govde `base_model`'den yuklenir.
Bu yapilandirma yalnizca hangi katmanlarin lineerlestirildigini ve Gated DeltaNet
modullerinin nasil kurulacagini tanimlar. Agirliklar iki kaynaktan gelir:
- govde (32 softmax katmani + gomme/LM basi) : `base_model` deposundan
- 8 GDN katmani : bu deponun gdn_weights.safetensors
"""
model_type = "erk_linear"
def __init__(
self,
base_model: str = "ecloudtech/Erk-14B",
gdn_layers=None,
hidden_size: int = 5120,
num_hidden_layers: int = 40,
gdn_head_dim: int = 128,
gdn_num_heads: int = 40,
gdn_use_gate: bool = True,
gdn_use_short_conv: bool = True,
gdn_mode: str = "chunk",
gdn_weights_file: str = "gdn_weights.safetensors",
**kwargs,
):
self.base_model = base_model
self.gdn_layers = list(gdn_layers) if gdn_layers is not None else [1, 3, 5, 7, 10, 36, 38, 39]
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.gdn_head_dim = gdn_head_dim
self.gdn_num_heads = gdn_num_heads
self.gdn_use_gate = gdn_use_gate
self.gdn_use_short_conv = gdn_use_short_conv
self.gdn_mode = gdn_mode
self.gdn_weights_file = gdn_weights_file
super().__init__(**kwargs)
@property
def linear_ratio(self) -> float:
"""Lineerlestirilen dikkat katmanlarinin orani (8/40 = 0.20)."""
return len(self.gdn_layers) / float(self.num_hidden_layers)
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