diff --git a/bytelatent/args.py b/bytelatent/args.py
index 8ee5a67..ebe43ba 100644
--- a/bytelatent/args.py
+++ b/bytelatent/args.py
@@ -260,6 +260,9 @@ class ValidationArgs(BaseModel):
     max_n_docs: int | None = (
         None  # If None the whole validation file is used -> /!\ This number of steps is gpu dependent (100 max steps on 8 gpus = 800 steps on 1 gpu)
     )
+    max_n_batches: int | None = (
+        None  # If None the whole validation file is used -> /!\ This number of steps is gpu dependent (100 max steps on 8 gpus = 800 steps on 1 gpu)
+    )
     use_val_from_train_src: bool = True  # Use the validation set from training sources
     root_dir: str = ""
     sources: list[str] = []  # Other sources to eval on
diff --git a/bytelatent/eval.py b/bytelatent/eval.py
index 2401fc0..a917f48 100644
--- a/bytelatent/eval.py
+++ b/bytelatent/eval.py
@@ -153,6 +153,7 @@ def eval_ppl_on_path(
     path: str,
     arrow_batch_size: int,
     max_n_docs: int | None,
+    max_n_batches: int | None,
     s3_profile: str | None = None,
 ):
     model.eval()
@@ -189,7 +190,9 @@ def eval_ppl_on_path(
     total_loss = 0.0
     n_bytes = 0
     batch_iterator = packing_iterator.create_iter()
-    for batch in batch_iterator:
+    for i, batch in enumerate(batch_iterator):
+        if i == max_n_batches:
+            break
         x = torch.from_numpy(batch.x).cuda()
         y = torch.from_numpy(batch.y).cuda()
         mask = None if batch.mask is None else torch.from_numpy(batch.mask).cuda()
@@ -203,7 +206,7 @@ def eval_ppl_on_path(
                 pred = model(x, patch_lengths=patch_lengths)
             else:
                 pred = model(x)
-            loss = F.cross_entropy(pred.flatten(0, 1), y.flatten(0, 1), reduction="sum")
+            loss = F.cross_entropy(pred.flatten(0, 1), y.flatten(0, 1), reduction="sum", ignore_index=0)
             total_loss += loss.item()
         else:
             raise NotImplementedError()
@@ -301,6 +304,7 @@ def launch_eval(eval_args: EvalArgs):
                     add_patches=train_cfg.data.add_patches,
                     path=os.path.join(eval_args.validation.root_dir, source),
                     max_n_docs=eval_args.validation.max_n_docs,
+                    max_n_batches=eval_args.validation.max_n_batches,
                     arrow_batch_size=20,
                     s3_profile=eval_args.s3_profile,
                 )