diff --git a/config/gpt2_small_fast_supervised.yaml b/config/gpt2_small_fast_supervised.yaml
index d71e1267e..93675366d 100644
--- a/config/gpt2_small_fast_supervised.yaml
+++ b/config/gpt2_small_fast_supervised.yaml
@@ -15,6 +15,7 @@ data:
 supervised_data:
   validation_urls:
     - "gs://marin-us-central2/benchmarks/mmlu/mmlu-*-dev-evaluation.jsonl.gz"
+    - "gs://marin-us-central2/benchmarks/mmlu/mmlu-*-validation-evaluation.jsonl.gz"
   cache_dir: "gs://marin-us-central2/benchmarks/tokenized-gpt2/mmlu/"
   input_field: "input"
   output_field: "output"
diff --git a/src/levanter/data/text.py b/src/levanter/data/text.py
index 70c1fe4b3..f2bea44b2 100644
--- a/src/levanter/data/text.py
+++ b/src/levanter/data/text.py
@@ -631,7 +631,7 @@ def mk_supervised_dataset(config: LMSupervisedDatasetConfig, tokenizer: PreTrain
     input_field = config.input_field
     output_field = config.output_field
 
-    output_exemplar = {"input_ids": np.zeros((0,), dtype=np.int32), "sources_len": np.zeros((), dtype=np.int32)}
+    output_exemplar = {"input_ids": np.zeros((0,), dtype=np.int32), "sources_len": np.zeros((0,), dtype=np.int32)}
 
     dataset = dataset.map_batches(lambda ex: preprocess_supervised_example(ex, tokenizer, input_field, output_field), batch_size=128, num_cpus=num_cpus_used_by_tokenizer(tokenizer), output_exemplar=output_exemplar)  # type: ignore
     dataset = dataset.build_or_load_cache(config.cache_dir, await_finished=True)  # type: ignore
diff --git a/src/levanter/main/train_lm.py b/src/levanter/main/train_lm.py
index fe5e5dd35..79095d601 100644
--- a/src/levanter/main/train_lm.py
+++ b/src/levanter/main/train_lm.py
@@ -160,13 +160,13 @@ def main(config: TrainLmConfig):
 
         levanter.tracker.log_summary({"parameter_count": parameter_count(state.model)})
 
+        max_eval_examples_per_ds = config.trainer.max_eval_batches
+        if max_eval_examples_per_ds is not None:
+            max_eval_examples_per_ds *= config.trainer.eval_batch_size
+
         if len(tagged_eval_datasets) == 0:
             logger.warning("No evaluation datasets provided.")
         else:
-            max_eval_examples_per_ds = config.trainer.max_eval_batches
-            if max_eval_examples_per_ds is not None:
-                max_eval_examples_per_ds *= config.trainer.eval_batch_size
-
             causal_datasets = [
                 (CausalLmDataset(ds, Pos, KeyPos, ignore_index=config.data.ignore_token_id), tags)
                 for ds, tags in tagged_eval_datasets