diff --git a/recommendation/dlrm/pytorch/python/criteo.py b/recommendation/dlrm/pytorch/python/criteo.py
index de08b82f1..77c7184c2 100755
--- a/recommendation/dlrm/pytorch/python/criteo.py
+++ b/recommendation/dlrm/pytorch/python/criteo.py
@@ -253,7 +253,10 @@ def load_query_samples(self, sample_list):
                 s = self.random_offsets[l]
                 e = self.random_offsets[l+1]
 
-            ls = [self.test_data[i] for i in range(s, e)]
+            if self.use_mlperf_bin_loader and self.samples_to_aggregate > 1:
+                ls = [self.test_data[l]]
+            else:
+                ls = [self.test_data[i] for i in range(s, e)]
             if self.use_mlperf_bin_loader:
                 # NOTE: in binary dataset the values are transformed
                 ls_t = list(zip(*ls))
diff --git a/v0.5/recommendation/python/backend_onnxruntime.py b/v0.5/recommendation/python/backend_onnxruntime.py
index 189484da8..6c1198a84 100755
--- a/v0.5/recommendation/python/backend_onnxruntime.py
+++ b/v0.5/recommendation/python/backend_onnxruntime.py
@@ -45,7 +45,16 @@ def predict(self, batch_dense_X, batch_lS_o, batch_lS_i):
         # print("onnx predict")
         # print(self.inputs)
         # print(self.outputs)
-        
+
+        '''
+        incoming_bs = batch_dense_X.shape[0]
+        model_saved_bs = 2048
+        if (incoming_bs != model_saved_bs):
+            print("WARNING: mismatch beween incoming " + str(incoming_bs) + " and model saved " + str(model_saved_bs) + " mini-batch size")
+            fake_output = torch.zeros(size=(incoming_bs,1), dtype=torch.float32)
+            return fake_output
+        '''
+
         dict_inputs = {}
 
         # Dmitriy's approach to build dictionaries