diff --git a/src/llamafactory/data/preprocess.py b/src/llamafactory/data/preprocess.py
index 7bf9d4bc..4123645f 100644
--- a/src/llamafactory/data/preprocess.py
+++ b/src/llamafactory/data/preprocess.py
@@ -74,19 +74,21 @@ def preprocess_supervised_dataset(
     if processor is not None:
         model_inputs["pixel_values"] = []
         preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
+        if hasattr(processor, "image_seq_length"):  # paligemma models
+            model_inputs["token_type_ids"] = []
 
     for i in range(len(examples["prompt"])):
         if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
             logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
             continue
 
-        if processor is not None and not hasattr(processor, "image_seq_length"):  # llava case
+        if processor is not None and not hasattr(processor, "image_seq_length"):  # llava models
             examples["prompt"][i][0]["content"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
 
         messages = examples["prompt"][i] + examples["response"][i]
         input_ids, labels = [], []
 
-        if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma case
+        if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma models
             image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
             input_ids += [image_token_id] * getattr(processor, "image_seq_length")
             labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
@@ -120,6 +122,10 @@ def preprocess_supervised_dataset(
         model_inputs["labels"].append(labels)
         if processor is not None:
             model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))
+            if hasattr(processor, "image_seq_length"):  # paligemma models
+                token_type_ids = [0] * getattr(processor, "image_seq_length")
+                token_type_ids += [1] * (len(input_ids) - getattr(processor, "image_seq_length"))
+                model_inputs["token_type_ids"].append(token_type_ids)
 
     return model_inputs
 
@@ -183,13 +189,15 @@ def preprocess_unsupervised_dataset(
     if processor is not None:
         model_inputs["pixel_values"] = []
         preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
+        if hasattr(processor, "image_seq_length"):  # paligemma models
+            model_inputs["token_type_ids"] = []
 
     for i in range(len(examples["prompt"])):
         if len(examples["prompt"][i]) % 2 != 1:
             logger.warning("Dropped invalid example: {}".format(examples["prompt"][i] + examples["response"][i]))
             continue
 
-        if processor is not None and not hasattr(processor, "image_seq_length"):  # llava case
+        if processor is not None and not hasattr(processor, "image_seq_length"):  # llava models
             examples["prompt"][i][0]["content"] = IMAGE_TOKEN + examples["prompt"][i][0]["content"]
 
         if len(examples["response"][i]) == 1:
@@ -209,7 +217,7 @@ def preprocess_unsupervised_dataset(
         if template.efficient_eos:
             labels += [tokenizer.eos_token_id]
 
-        if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma case
+        if processor is not None and hasattr(processor, "image_seq_length"):  # paligemma models
             image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
             input_ids = [image_token_id] * getattr(processor, "image_seq_length") + input_ids