diff --git a/example.py b/example.py
new file mode 100644
index 0000000..0a2eb66
--- /dev/null
+++ b/example.py
@@ -0,0 +1,48 @@
+import torch
+from open_mythos.main import OpenMythos, MythosConfig
+
+
+attn_type = "mla"  # or "gqa"
+
+base = {
+    "vocab_size": 1000,
+    "dim": 256,
+    "n_heads": 8,
+    "max_seq_len": 128,
+    "max_loop_iters": 4,
+    "prelude_layers": 1,
+    "coda_layers": 1,
+    "n_experts": 8,
+    "n_shared_experts": 1,
+    "n_experts_per_tok": 2,
+    "expert_dim": 64,
+    "lora_rank": 8,
+    "attn_type": attn_type,
+}
+
+if attn_type == "gqa":
+    cfg = MythosConfig(**base, n_kv_heads=2)
+else:
+    cfg = MythosConfig(
+        **base,
+        n_kv_heads=8,
+        kv_lora_rank=32,
+        q_lora_rank=64,
+        qk_rope_head_dim=16,
+        qk_nope_head_dim=16,
+        v_head_dim=16,
+    )
+
+model = OpenMythos(cfg)
+total = sum(p.numel() for p in model.parameters())
+print(f"\n[{attn_type.upper()}] Parameters: {total:,}")
+
+ids = torch.randint(0, cfg.vocab_size, (2, 16))
+logits = model(ids, n_loops=4)
+print(f"[{attn_type.upper()}] Logits shape: {logits.shape}")
+
+out = model.generate(ids, max_new_tokens=8, n_loops=8)
+print(f"[{attn_type.upper()}] Generated shape: {out.shape}")
+
+A = model.recurrent.injection.get_A()
+print(f"[{attn_type.upper()}] Spectral radius ρ(A) max: {A.max().item():.4f} (must be < 1)")
diff --git a/open_mythos/main.py b/open_mythos/main.py
index 77be9d8..aa2f878 100644
--- a/open_mythos/main.py
+++ b/open_mythos/main.py
@@ -11,7 +11,6 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-
 # ---------------------------------------------------------------------------
 # Config
 # ---------------------------------------------------------------------------
@@ -1014,65 +1013,3 @@ class OpenMythos(nn.Module):
             input_ids = torch.cat([input_ids, next_tok], dim=1)
         return input_ids
 
-
-# ---------------------------------------------------------------------------
-# Quick smoke test
-# ---------------------------------------------------------------------------
-
-
-def _smoke(attn_type: str) -> None:
-    """
-    Instantiate a small OpenMythos model with the given attention type, run a
-    forward pass and a short generation, and verify the LTI spectral radius.
-
-    Args:
-        attn_type -- "gqa" or "mla"
-    """
-    base = dict(
-        vocab_size=1000,
-        dim=256,
-        n_heads=8,
-        max_seq_len=128,
-        max_loop_iters=4,
-        prelude_layers=1,
-        coda_layers=1,
-        n_experts=8,
-        n_shared_experts=1,
-        n_experts_per_tok=2,
-        expert_dim=64,
-        lora_rank=8,
-        attn_type=attn_type,
-    )
-    if attn_type == "gqa":
-        cfg = MythosConfig(**base, n_kv_heads=2)
-    else:
-        cfg = MythosConfig(
-            **base,
-            n_kv_heads=8,  # unused by MLA but field must be valid
-            kv_lora_rank=32,
-            q_lora_rank=64,
-            qk_rope_head_dim=16,
-            qk_nope_head_dim=16,
-            v_head_dim=16,
-        )
-
-    model = OpenMythos(cfg)
-    total = sum(p.numel() for p in model.parameters())
-    print(f"\n[{attn_type.upper()}] Parameters: {total:,}")
-
-    ids = torch.randint(0, cfg.vocab_size, (2, 16))
-    logits = model(ids, n_loops=4)
-    print(f"[{attn_type.upper()}] Logits shape: {logits.shape}")
-
-    out = model.generate(ids, max_new_tokens=8, n_loops=8)
-    print(f"[{attn_type.upper()}] Generated shape: {out.shape}")
-
-    A = model.recurrent.injection.get_A()
-    print(
-        f"[{attn_type.upper()}] Spectral radius ρ(A) max: {A.max().item():.4f} (must be < 1)"
-    )
-
-
-if __name__ == "__main__":
-    _smoke("gqa")
-    _smoke("mla")