diff --git a/predict.py b/predict.py
index ab3115e..4954973 100644
--- a/predict.py
+++ b/predict.py
@@ -1,12 +1,12 @@
 # Prediction interface for Cog ⚙️
 # https://github.com/replicate/cog/blob/main/docs/python.md
 
-from cog import BasePredictor, Input, Path
+from cog import BasePredictor, Input, Path, ConcatenateIterator
 import os
 import torch
 from threading import Thread
-from transformers import AutoModelForCausalLM
 from deepseek_vl.utils.io import load_pil_images
+from transformers import AutoModelForCausalLM, TextIteratorStreamer
 from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
 
 # Enable faster download speed
@@ -34,9 +34,9 @@ class Predictor(BasePredictor):
     def predict(
         self,
         image: Path = Input(description="Input image"),
-        prompt: str = Input(description="Input prompt", default="Describe the image"),
+        prompt: str = Input(description="Input prompt", default="Describe this image"),
         max_new_tokens: int = Input(description="Maximum number of tokens to generate", default=512)
-    ) -> str:
+    ) -> ConcatenateIterator[str]:
         """Run a single prediction on the model"""
         conversation = [
             {
@@ -57,21 +57,26 @@ class Predictor(BasePredictor):
             images=pil_images,
             force_batchify=True
         ).to('cuda')
-
-        # run image encoder to get the image embeddings
-        inputs_embeds = self.vl_gpt.prepare_inputs_embeds(**prepare_inputs)
-
-        # run the model to get the response
-        outputs = self.vl_gpt.language_model.generate(
-            inputs_embeds=inputs_embeds,
-            attention_mask=prepare_inputs.attention_mask,
-            pad_token_id=self.tokenizer.eos_token_id,
-            bos_token_id=self.tokenizer.bos_token_id,
-            eos_token_id=self.tokenizer.eos_token_id,
-            max_new_tokens=max_new_tokens,
-            do_sample=False,
-            use_cache=True
+    
+        streamer = TextIteratorStreamer(
+            self.tokenizer, skip_prompt=True, skip_special_tokens=True
         )
-
-        answer = self.tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
-        return answer
+        
+        thread = Thread(
+            target=self.vl_gpt.language_model.generate,
+            kwargs={
+                "inputs_embeds": self.vl_gpt.prepare_inputs_embeds(**prepare_inputs),
+                "attention_mask": prepare_inputs.attention_mask,
+                "pad_token_id": self.tokenizer.eos_token_id,
+                "bos_token_id": self.tokenizer.bos_token_id,
+                "eos_token_id": self.tokenizer.eos_token_id,
+                "max_new_tokens": max_new_tokens,
+                "do_sample": False,
+                "use_cache": True,
+                "streamer": streamer,
+            },
+        )
+        thread.start()
+        for new_token in streamer:
+            yield new_token
+        thread.join()