diff --git a/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py b/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py
index 60f882fa9821..be1edd66aeb0 100644
--- a/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py
+++ b/examples/multimodal/multimodal_llm/neva/sequence_packing/preprocess_dataset.py
@@ -243,37 +243,9 @@ def pack_sequence(args, seq_lens):
     bins = packing_fn(seq_lens, args.max_seq_length)
     return bins
 
-
-def main():
-    torch.multiprocessing.set_sharing_strategy('file_system')
-
-    args = get_args()
-    nemo_config = OmegaConf.load(args.hparams_file)
-    nemo_config.model.mm_cfg.vision_encoder.from_pretrained = args.hf_vision_encoder
-    nemo_config.model.data.data_path = args.data_path
-    nemo_config.model.data.image_folder = args.image_folder
-    nemo_config.model.data.conv_template = args.conv_template
-    nemo_config.model.data.image_aspect_ratio = args.image_aspect_ratio
-
-    tokenizer = get_nmt_tokenizer(
-        library="sentencepiece",
-        tokenizer_model=args.tokenizer_path,
-    )
-    image_processor = create_image_processor(nemo_config.model.mm_cfg)
-    train_ds = make_supervised_data_module(
-        tokenizer=tokenizer, image_processor=image_processor, model_cfg=nemo_config.model
-    )["train_dataset"]
-    train_dl = DataLoader(train_ds, num_workers=32, collate_fn=None, shuffle=False)
-    # Example shape: {'tokens': torch.Size([1, 344]), 'labels': torch.Size([1, 344]), 'image': torch.Size([1, 1, 3, 224, 224])}
-
-    output_dir = args.output_dir
-    os.makedirs(output_dir, exist_ok=True)
-    logging.info(f"Output directory: {output_dir}")
-
-    prefix_path = f"{output_dir}/packed_seq_dataset"
-    # Original Datasets to Sequence Lengths Files
+def process_data_file(train_dl, prefix_path, data_file):
     builders = {}
-    for item_dict in tqdm(train_dl, desc="Building indexed datasets"):
+    for item_dict in tqdm(train_dl, desc=f"Building indexed datasets for {data_file}"):
         item_dict = {k: v[0] for k, v in item_dict.items()}
         seq_len = len(item_dict['tokens'])
         if seq_len in builders:
@@ -294,7 +266,7 @@ def main():
         logging.info(f"Finalizing builder for sequence length {seq_len} at {idx_path}")
         builder.finalize(idx_path)
 
-    # Packing Sequences into Bins
+def pack_sequences_into_bins(args, output_dir, prefix_path):
     files = os.listdir(f"{output_dir}/packed_seq_dataset")
     pattern = rf"seqlen_(\d+).bin"
     seq_len_list = []
@@ -303,16 +275,22 @@ def main():
         if match:
             seq_len = int(match.group(1))
             seq_len_list.append(seq_len)
-
+    
     aggregated_seq_lens = []
     doc_pop_order = {}
     indexed_datasets = {}
+    error_len = 0
     for seq_len in seq_len_list:
         dataset_path = f"{prefix_path}/seqlen_{seq_len}"
-        dataset = IndexedDataset(dataset_path, multimodal=True)
-        aggregated_seq_lens.extend([seq_len] * (len(dataset.document_indices) - 1))
-        doc_pop_order[seq_len] = list(np.random.permutation(len(dataset.document_indices) - 1))
-        indexed_datasets[seq_len] = dataset
+        try:
+            dataset = IndexedDataset(dataset_path, multimodal=True)
+            aggregated_seq_lens.extend([seq_len] * (len(dataset.document_indices) - 1))
+            doc_pop_order[seq_len] = list(np.random.permutation(len(dataset.document_indices) - 1))
+            indexed_datasets[seq_len] = dataset
+        except Exception as e:
+            error_len += 1
+            logging.error(f"Error while processing {dataset_path}: {e}")
+    logging.info(f"Number of errors: {error_len}")
 
     logging.info("Getting bins")
     bins = pack_sequence(args, aggregated_seq_lens)
@@ -323,7 +301,6 @@ def main():
     avg_bins_sum = sum([sum(x) for x in bins]) / num_bins
     logging.info(f"Number of bins: {num_bins}, Average bin length: {avg_bins_len}, Average bin sum: {avg_bins_sum}")
 
-    # Reading Sequence Lengths and Packing into New Files
     final_builder_path = get_bin_path(f"{prefix_path}")
     logging.info(f"Creating final builder at {final_builder_path}")
     final_builder = IndexedDatasetBuilder(final_builder_path, dtype=np.float32, multimodal=True)
@@ -356,6 +333,41 @@ def main():
     final_builder.finalize(idx_path)
     logging.info(f"Number of bins: {num_bins}, Average bin length: {avg_bins_len}, Average bin sum: {avg_bins_sum}")
 
+def main():
+    torch.multiprocessing.set_sharing_strategy('file_system')
+
+    args = get_args()
+    nemo_config = OmegaConf.load(args.hparams_file)
+    nemo_config.model.mm_cfg.vision_encoder.from_pretrained = args.hf_vision_encoder
+    nemo_config.model.data.data_path = args.data_path
+    nemo_config.model.data.image_folder = args.image_folder
+    nemo_config.model.data.conv_template = args.conv_template
+    nemo_config.model.data.image_aspect_ratio = args.image_aspect_ratio
+
+    tokenizer = get_nmt_tokenizer(
+        library="sentencepiece",
+        tokenizer_model=args.tokenizer_path,
+    )
+    image_processor = create_image_processor(nemo_config.model.mm_cfg)
+    output_dir = args.output_dir
+    os.makedirs(output_dir, exist_ok=True)
+    logging.info(f"Output directory: {output_dir}")
 
+    prefix_path = f"{output_dir}/packed_seq_dataset"
+    os.makedirs(prefix_path, exist_ok=True)
+    
+    data_files = nemo_config.model.data.data_file_names
+    for data_file in data_files:
+        logging.info(f"Processing data file: {data_file}")
+        
+        train_ds = make_supervised_data_module(
+            tokenizer=tokenizer, image_processor=image_processor, model_cfg=nemo_config.model, data_file=data_file
+        )["train_dataset"]
+        train_dl = DataLoader(train_ds, num_workers=32, collate_fn=None, shuffle=False)
+        
+        process_data_file(train_dl, prefix_path, data_file)
+    
+    pack_sequences_into_bins(args, output_dir, prefix_path)
+    
 if __name__ == "__main__":
     main()