Update evo_transformer.py

evo_transformer.py

@@ -0,0 +1,48 @@
+# evo_transformer.py
+import random
+
+class EvoTransformer:
+    def __init__(self, config=None):
+        # Initialize with default or passed config
+        self.config = config or {
+            "layers": 4,
+            "attention_heads": 4,
+            "ffn_dim": 1024,
+            "dropout": 0.1,
+            "memory": False,
+        }
+        self.history = [self.config.copy()]
+
+    def mutate(self):
+        new_config = self.config.copy()
+        trait = random.choice(list(new_config.keys()))
+        
+        if trait == "layers":
+            new_config[trait] = max(1, new_config[trait] + random.choice([-1, 1]))
+        elif trait == "attention_heads":
+            new_config[trait] = random.choice([2, 4, 6, 8])
+        elif trait == "ffn_dim":
+            new_config[trait] = random.choice([512, 1024, 2048])
+        elif trait == "dropout":
+            new_config[trait] = round(min(max(0.0, new_config[trait] + random.uniform(-0.05, 0.05)), 0.5), 2)
+        elif trait == "memory":
+            new_config[trait] = not new_config[trait]
+
+        self.config = new_config
+        self.history.append(new_config.copy())
+
+    def evolve(self, generations=3):
+        for _ in range(generations):
+            self.mutate()
+
+    def get_history(self):
+        return self.history
+
+    def evaluate(self):
+        # Simulate an accuracy score for demo purposes
+        score = round(random.uniform(0.85, 0.95), 4)
+        return {"accuracy": score, "params": self.estimate_params()}
+
+    def estimate_params(self):
+        # Simulated parameter count based on config
+        return 10 + self.config["layers"] * self.config["ffn_dim"] * 0.001