diff --git a/scripts/base_train.py b/scripts/base_train.py
index ddd2c98..4e02556 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -273,7 +273,7 @@ for step in range(num_iterations + 1):
         x, y = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
     # gradient clipping (TODO possibly experiment with)
     if grad_clip > 0.0:
-        torch.nn.utils.clip_grad_norm_(orig_model.parameters(), grad_clip)
+        grad_norm = torch.nn.utils.clip_grad_norm_(orig_model.parameters(), grad_clip)
     # step the optimizers
     lrm = get_lr_multiplier(step)
     for opt in optimizers:
@@ -300,13 +300,14 @@ for step in range(num_iterations + 1):
     mfu = 100 * flops_per_sec / promised_flops_per_sec_h100 # in %
     if step > 10:
         total_training_time += dt # only count the time after the first 10 steps
-    print0(f"step {step:05d}/{num_iterations:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | total time: {total_training_time/60:.2f}m")
+    print0(f"step {step:05d}/{num_iterations:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | grad_norm: {grad_norm.item():.5f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | total time: {total_training_time/60:.2f}m")
     if step % 100 == 0:
         wandb_run.log({
             "step": step,
             "total_training_flops": flops_so_far,
             "total_training_time": total_training_time,
             "train/loss": debiased_smooth_loss,
+            "train/grad_norm": grad_norm.item(),
             "train/lrm": lrm,
             "train/dt": dt,
             "train/tok_per_sec": tok_per_sec,