Update evo_model.py

evo_model.py

@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class EvoTransformerV22(nn.Module):
+    def __init__(self, d_model=384, num_heads=6, ffn_dim=1024, num_layers=6, memory_enabled=False):
+        super().__init__()
+        self.embedding = nn.Embedding(30522, d_model)
+        self.memory_enabled = memory_enabled
+        self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model)) if memory_enabled else None
+
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=num_heads,
+            dim_feedforward=ffn_dim,
+            batch_first=True
+        )
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.pool = nn.AdaptiveAvgPool1d(1)
+
+        self.classifier = nn.Sequential(
+            nn.Linear(d_model, 128),
+            nn.ReLU(),
+            nn.Linear(128, 2)  # Binary classification
+        )
+
+    def forward(self, input_ids):
+        x = self.embedding(input_ids)
+
+        if self.memory_enabled and self.memory_token is not None:
+            mem = self.memory_token.expand(x.size(0), 1, x.size(2))
+            x = torch.cat([mem, x], dim=1)
+
+        x = self.transformer(x)
+        x = self.pool(x.transpose(1, 2)).squeeze(-1)
+        return self.classifier(x)