Update evo_model.py

evo_model.py

@@ -3,17 +3,18 @@ import torch.nn as nn
 from torch.nn import TransformerEncoder, TransformerEncoderLayer
 
 class EvoTransformer(nn.Module):
-    def __init__(self, vocab_size=30522, d_model=384, nhead=6, num_layers=6, dim_feedforward=1024, dropout=0.1, num_labels=2):
+    def __init__(self, vocab_size=30522, d_model=512, nhead=8, num_layers=6, dim_feedforward=1024, dropout=0.1):
         super(EvoTransformer, self).__init__()
         self.embedding = nn.Embedding(vocab_size, d_model)
         self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
-
+        
         encoder_layer = TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout)
         self.transformer = TransformerEncoder(encoder_layer, num_layers=num_layers)
-        self.norm = nn.LayerNorm(d_model)
-
+        
         self.memory_proj = nn.Linear(d_model, d_model)
-        self.classifier = nn.Linear(d_model, num_labels)
+        self.norm = nn.LayerNorm(d_model)
+        
+        self.classifier = nn.Linear(d_model, 1)  # Matches saved model: output is a single logit
 
     def forward(self, input_ids):
         x = self.embedding(input_ids)
@@ -25,6 +26,4 @@ class EvoTransformer(nn.Module):
         x = self.norm(x)
 
         memory_output = self.memory_proj(x[:, 0])
-        logits = self.classifier(memory_output)
-
-        return logits
+        return memory_output