diff --git a/src/heretic/main.py b/src/heretic/main.py
index 0a7bbc7..5034e3a 100644
--- a/src/heretic/main.py
+++ b/src/heretic/main.py
@@ -27,7 +27,7 @@ from rich.traceback import install
 from .config import Settings
 from .evaluator import Evaluator
 from .model import AbliterationParameters, Model
-from .utils import get_readme_intro, load_prompts, print
+from .utils import format_duration, get_readme_intro, load_prompts, print
 
 
 def run():
@@ -178,6 +178,7 @@ def run():
     )
 
     trial_index = 0
+    start_time = time.perf_counter()
 
     def objective(trial: optuna.Trial):
         nonlocal trial_index
@@ -232,6 +233,17 @@ def run():
         print("* Evaluating...")
         score, kl_divergence, refusals = evaluator.get_score()
 
+        elapsed_time = time.perf_counter() - start_time
+        remaining_time = (elapsed_time / trial_index) * (
+            settings.n_trials - trial_index
+        )
+        print()
+        print(f"[grey50]Elapsed time: [bold]{format_duration(elapsed_time)}[/][/]")
+        if trial_index < settings.n_trials:
+            print(
+                f"[grey50]Estimated remaining time: [bold]{format_duration(remaining_time)}[/][/]"
+            )
+
         trial.set_user_attr("kl_divergence", kl_divergence)
         trial.set_user_attr("refusals", refusals)
         trial.set_user_attr("parameters", parameters)
diff --git a/src/heretic/utils.py b/src/heretic/utils.py
index 6f0fdfb..6cb07fe 100644
--- a/src/heretic/utils.py
+++ b/src/heretic/utils.py
@@ -21,6 +21,19 @@ from .config import DatasetSpecification, Settings
 print = Console(highlight=False).print
 
 
+def format_duration(seconds: float) -> str:
+    seconds = round(seconds)
+    hours, seconds = divmod(seconds, 3600)
+    minutes, seconds = divmod(seconds, 60)
+
+    if hours > 0:
+        return f"{hours}h {minutes}m"
+    elif minutes > 0:
+        return f"{minutes}m {seconds}s"
+    else:
+        return f"{seconds}s"
+
+
 def load_prompts(specification: DatasetSpecification) -> list[str]:
     dataset = load_dataset(specification.dataset, split=specification.split)
     return list(dataset[specification.column])