Update evo_model.py

evo_model.py

@@ -1,3 +1,5 @@
+# evo_model.py
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -7,6 +9,7 @@ class EvoEncoder(nn.Module):
         super().__init__()
         self.embedding = nn.Embedding(30522, d_model)
         self.memory_enabled = memory_enabled
+
         if memory_enabled:
             self.memory_proj = nn.Linear(d_model, d_model)
             self.memory_token = nn.Parameter(torch.zeros(1, 1, d_model))
@@ -31,14 +34,36 @@ class EvoEncoder(nn.Module):
         x = self.transformer(x)
         return x
 
+
 class EvoTransformerV22(nn.Module):
-    def __init__(self):
+    def __init__(self, config=None):
         super().__init__()
-        self.encoder = EvoEncoder(d_model=512, num_heads=8, ffn_dim=1024, num_layers=6, memory_enabled=True)
+
+        # Default architecture if no config is passed
+        if config is None:
+            config = {
+                "num_layers": 6,
+                "ffn_dim": 1024,
+                "num_heads": 8,
+                "memory_enabled": True
+            }
+
+        self.encoder = EvoEncoder(
+            d_model=512,
+            num_heads=config["num_heads"],
+            ffn_dim=config["ffn_dim"],
+            num_layers=config["num_layers"],
+            memory_enabled=config["memory_enabled"]
+        )
         self.pool = nn.AdaptiveAvgPool1d(1)
-        self.classifier = nn.Linear(512, 1)  # ✅ Matches checkpoint
+        self.classifier = nn.Linear(512, 1)
 
     def forward(self, input_ids):
         x = self.encoder(input_ids)
         x = self.pool(x.transpose(1, 2)).squeeze(-1)
-        return self.classifier(x)  # Output: [batch_size, 1]
+        return self.classifier(x)
+
+
+# ✅ For loading models dynamically during mutation or feedback retrain
+def build_model_from_config(config):
+    return EvoTransformerV22(config)