diff --git a/scripts/example.py b/scripts/example.py
new file mode 100644
index 0000000..dc8b391
--- /dev/null
+++ b/scripts/example.py
@@ -0,0 +1,138 @@
+import os
+from typing import Dict, Tuple, List, Optional
+
+import coremltools as ct
+import onnxruntime
+import torch
+import torch.nn.functional as F
+import json
+from PIL import Image
+from uform.models import PreProcessor, VLM
+from functools import partial
+import time
+
+# export TOKENIZERS_PARALLELISM=true
+
+def preprocess_data(func):
+    def wrapper(self, *args, **kwargs):
+        result = func(self, *args, **kwargs)
+        if self.method == "onnx":
+            result = {k: v.cpu().numpy() for k, v in result[0].items()}, {
+                self.input_image_name: result[1].cpu().numpy()
+            }
+        elif self.method == "coreml":
+            input_ids = result[0]["input_ids"].type(torch.int32).cpu().numpy()
+            attention_mask = result[0]["attention_mask"].type(torch.int32).cpu().numpy()
+
+            result = {"input_ids": input_ids, "attention_mask": attention_mask}, {
+                self.input_image_name: result[1].cpu().numpy()
+            }
+        return result
+
+    return wrapper
+
+def get_local_model(model_name: str, token: Optional[str] = None) -> VLM:
+    config_path = f"{model_name}/torch_config.json"
+    state = torch.load(f"{model_name}/torch_weight.pt")
+
+    tokenizer_path = f"{model_name}/tokenizer.json"
+
+    with open(config_path, "r") as f:
+        model = VLM(json.load(f), tokenizer_path)
+
+    model.image_encoder.load_state_dict(state["image_encoder"])
+    model.text_encoder.load_state_dict(state["text_encoder"])
+
+    return model.eval()
+
+
+class MyModel:
+    def __init__(self, method: str, model_fpath: str) -> None:
+        self.method = method
+        self.model_fpath = model_fpath
+        max_position_embeddings = 50
+        if method == "torch":
+            self.model = get_local_model(model_fpath)
+            self.image_model = partial(self.model.encode_image, return_features=True)
+            self.text_model = partial(self.model.encode_text, return_features=True)
+        elif method == "onnx":
+            fname = "multilingual.{}-encoder.onnx"
+            image_ort_session = onnxruntime.InferenceSession(
+                os.path.join(model_fpath, fname.format("image")), providers=["CPUExecutionProvider"]
+            )
+            text_ort_session = onnxruntime.InferenceSession(
+                os.path.join(model_fpath, fname.format("text")), providers=["CPUExecutionProvider"]
+            )
+            input_ids = text_ort_session.get_inputs()[0]
+            max_position_embeddings = input_ids.shape[-1]
+
+            def predict_func(ort_session, data):
+                out = ort_session.run(None, data)
+                return torch.tensor(out[0]), torch.tensor(out[1])
+
+            self.image_model = partial(predict_func, image_ort_session)
+            self.text_model = partial(predict_func, text_ort_session)
+
+            input_image = image_ort_session.get_inputs()[0]
+            self.input_image_name = input_image.name
+        elif method == "coreml":
+            fname = "multilingual-v2.{}-encoder.mlpackage"
+            image_mlmodel = ct.models.MLModel(os.path.join(model_fpath, fname.format("image")))
+            text_mlmodel = ct.models.MLModel(os.path.join(model_fpath, fname.format("text")))
+
+            def predict_func(model, data):
+                out = model.predict(data)
+                return torch.tensor(out["features"]), torch.tensor(out["embeddings"])
+
+            self.image_model = partial(predict_func, image_mlmodel)
+            self.text_model = partial(predict_func, text_mlmodel)
+
+            input_image = image_mlmodel.input_description._fd_spec[0]
+            input_text_lst = text_mlmodel.input_description._fd_spec
+            self.input_image_name = input_image.name
+
+        self.preprocess = PreProcessor(
+            os.path.join(self.model_fpath, "tokenizer.json"), max_position_embeddings, 1, 224
+        )
+
+    @preprocess_data
+    def preprocess_text_image(self, text, image) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
+        image_data = self.preprocess.preprocess_image(image)
+        text_data = self.preprocess.preprocess_text(text)
+        return text_data, image_data
+
+    def forward(self, text_data, image_data) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        image_features, image_embedding = self.image_model(image_data)
+        text_features, text_embedding = self.text_model(text_data)
+        return image_features, image_embedding, text_features, text_embedding
+
+    def __call__(self, text: str, image) -> float:
+        text_data, image_data = self.preprocess_text_image(text, image)
+        image_features, image_embedding = self.image_model(image_data)
+        text_features, text_embedding = self.text_model(text_data)
+
+        similarity = F.cosine_similarity(image_embedding, text_embedding)
+        if self.method == "torch":
+            joint_embedding = self.model.encode_multimodal(
+                image_features=image_features,
+                text_features=text_features,
+                attention_mask=text_data["attention_mask"],
+            )
+            score = self.model.get_matching_scores(joint_embedding)
+            print("torch score", score)
+        return similarity
+
+if __name__ == "__main__":
+    text = 'a small red panda in a zoo'
+    image = Image.open('red_panda.jpg')
+    model_fpath = ...
+
+    for method in ["torch", "onnx", "coreml"]:
+        model = MyModel(method, model_fpath)
+        text_data, image_data = model.preprocess_text_image(text, image)
+        model.forward(text_data, image_data) # just for warm-up
+        loop_cnt = 10
+        s1 = time.time()
+        for _ in range(loop_cnt):
+            model.forward(text_data, image_data)
+        print(method, time.time() - s1)
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