diff --git a/src/heretic/config.py b/src/heretic/config.py
index e217bba..8aa21c7 100644
--- a/src/heretic/config.py
+++ b/src/heretic/config.py
@@ -23,6 +23,11 @@ class DatasetSpecification(BaseModel):
 class Settings(BaseSettings):
     model: str = Field(description="Hugging Face model ID, or path to model on disk")
 
+    evaluate_model: str | None = Field(
+        default=None,
+        description="If this model ID or path is set, then instead of abliterating the main model, evaluate this model relative to the main model",
+    )
+
     dtypes: list[str] = Field(
         description="List of PyTorch dtypes to try when loading model tensors. If loading with a dtype fails, the next dtype in the list will be tried."
     )
diff --git a/src/heretic/evaluator.py b/src/heretic/evaluator.py
index 37f25b3..307632c 100644
--- a/src/heretic/evaluator.py
+++ b/src/heretic/evaluator.py
@@ -73,7 +73,10 @@ class Evaluator:
             ** self.settings.kl_score_shape
         )
 
-        if kl_divergence > self.settings.max_kl_divergence:
+        if (
+            self.settings.evaluate_model is None
+            and kl_divergence > self.settings.max_kl_divergence
+        ):
             print(" [yellow](constraint violation; aborting trial)[/]")
             return kl_score, kl_divergence, self.base_refusals
         else:
diff --git a/src/heretic/main.py b/src/heretic/main.py
index 788e50b..0a7bbc7 100644
--- a/src/heretic/main.py
+++ b/src/heretic/main.py
@@ -156,6 +156,17 @@ def run():
         settings.batch_size = best_batch_size
         print(f"* Chosen batch size: [bold]{settings.batch_size}[/]")
 
+    evaluator = Evaluator(settings, model)
+
+    if settings.evaluate_model is not None:
+        print()
+        print(f"Loading model [bold]{settings.evaluate_model}[/]...")
+        settings.model = settings.evaluate_model
+        model.reload_model()
+        print("* Evaluating...")
+        evaluator.get_score()
+        return
+
     print()
     print("Calculating per-layer refusal directions...")
     print("* Obtaining residuals for good prompts...")
@@ -166,8 +177,6 @@ def run():
         bad_residuals.mean(dim=0) - good_residuals.mean(dim=0), p=2, dim=1
     )
 
-    evaluator = Evaluator(settings, model)
-
     trial_index = 0
 
     def objective(trial: optuna.Trial):