diff --git a/projects/ShareGPT4V/inference-multi-turn.py b/projects/ShareGPT4V/inference-multi-turn.py
new file mode 100644
index 0000000..488e3ce
--- /dev/null
+++ b/projects/ShareGPT4V/inference-multi-turn.py
@@ -0,0 +1,124 @@
+import argparse
+from io import BytesIO
+
+import requests
+import torch
+from PIL import Image
+from transformers import TextStreamer
+
+from share4v.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                               DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX)
+from share4v.conversation import SeparatorStyle, conv_templates
+from share4v.mm_utils import (KeywordsStoppingCriteria,
+                              get_model_name_from_path, tokenizer_image_token)
+from share4v.model.builder import load_pretrained_model
+from share4v.utils import disable_torch_init
+
+
+def load_image(image_file):
+    if image_file.startswith('http') or image_file.startswith('https'):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+    else:
+        image = Image.open(image_file).convert('RGB')
+    return image
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(
+        args.model_path, args.model_base, model_name)
+
+    if 'llama-2' in model_name.lower():
+        conv_mode = "share4v_llama_2"
+    elif "v1" in model_name.lower():
+        conv_mode = "share4v_v1"
+    elif "mpt" in model_name.lower():
+        conv_mode = "mpt"
+    else:
+        conv_mode = "share4v_v0"
+
+    if args.conv_mode is not None and conv_mode != args.conv_mode:
+        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(
+            conv_mode, args.conv_mode, args.conv_mode))
+    else:
+        args.conv_mode = conv_mode
+
+    conv = conv_templates[args.conv_mode].copy()
+
+    image = load_image(args.image_file)
+    image_tensor = image_processor.preprocess(image, return_tensors='pt')[
+        'pixel_values'].half().cuda()
+
+    while True:
+        try:
+            inp = input(f"{conv.roles[0]}: ")
+        except EOFError:
+            inp = ""
+        if not inp:
+            print("exit...")
+            break
+
+        print(f"{conv.roles[1]}: ", end="")
+
+        if image is not None:
+            # first message
+            if model.config.mm_use_im_start_end:
+                inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
+            else:
+                inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
+            conv.append_message(conv.roles[0], inp)
+            image = None
+        else:
+            # later messages
+            conv.append_message(conv.roles[0], inp)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+
+        input_ids = tokenizer_image_token(
+            prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(
+            keywords, tokenizer, input_ids)
+        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=image_tensor,
+                do_sample=True,
+                temperature=0.2,
+                max_new_tokens=1024,
+                streamer=streamer,
+                use_cache=True,
+                stopping_criteria=[stopping_criteria])
+
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (
+            input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(
+                f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+
+
+        outputs = tokenizer.batch_decode(
+            output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+        conv.messages[-1][-1] = outputs
+        print()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="Lin-Chen/ShareGPT4V-7B")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-file", type=str, default="https://llava-vl.github.io/static/images/view.jpg")
+    parser.add_argument("--conv-mode", type=str, default=None)
+    args = parser.parse_args()
+    eval_model(args)