diff --git a/boost_qa/model.py b/boost_qa/model.py
new file mode 100644
index 0000000..2beeae1
--- /dev/null
+++ b/boost_qa/model.py
@@ -0,0 +1,44 @@
+### Install mPLUG-Owl from https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2#install
+
+import torch.nn as nn
+import torch
+
+from typing import List
+from PIL import Image
+
+from mplug_owl2.model.builder import load_pretrained_model
+
+from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
+from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+
+
+class QInstructScorer(nn.Module):
+    def __init__(self, boost=True, device="cuda:0"):
+        super().__init__()
+        tokenizer, model, image_processor, _ = load_pretrained_model("teowu/mplug_owl2_7b_448_qinstruct_preview_v0.2", None, "mplug_owl2", device=device)
+        prompt = "USER: <|image|>Rate the quality of the image.\nASSISTANT: "
+        
+        if not boost:
+            self.preferential_ids_ = [id_[1] for id_ in tokenizer(["good", "average", "poor"])["input_ids"]]
+            self.weight_tensor = torch.Tensor([1, 0.5, 0]).half().to(model.device)
+        else:
+            self.preferential_ids_ = [id_[1] for id_ in tokenizer(["good", "average", "poor", "high", "medium", "low", "fine", "acceptable", "bad"])["input_ids"]]
+            self.weight_tensor = torch.Tensor([1, 0.5, 0, 1, 0.5, 0, 1, 0.5, 0]).half().to(model.device) / 3.
+    
+        self.tokenizer = tokenizer
+        self.model = model
+        self.image_processor = image_processor
+        self.input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)
+        
+    def forward(self, image: List[Image.Image]):
+        with torch.inference_mode():
+            image_tensor = self.image_processor.preprocess(image, return_tensors="pt")["pixel_values"].half().to(self.model.device)
+            output_logits = self.model(self.input_ids.repeat(image_tensor.shape[0], 1),
+                        images=image_tensor)["logits"][:,-1, self.preferential_ids_]
+
+            return torch.softmax(output_logits, -1) @ self.weight_tensor
+    
+
+if __name__ == "__main__":
+    scorer = QInstructScorer(boost=False)
+    print(scorer([Image.open("fig/examples_211.jpg"),Image.open("fig/sausage.jpg")]).tolist())
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