diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..ff4fb52b945713da7c3ea32d08ad355a44ded8a2 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..3361b114c533459a27b068226a86b46f89fb8d2e
--- /dev/null
+++ b/app.py
@@ -0,0 +1,121 @@
+import os
+import random
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import gradio as gr
+
+from utils import load_json, init_logger
+from demo import ConversationalAgent, CustomTheme
+from download import download_model
+
+FOOD_EXAMPLES = "demo/food_for_demo.json"
+MODEL_PATH = "/home/user/lr35_ep10"
+# MODEL_PATH = "/root/xtuner/work_dirs/internvl_v2_internlm2_2b_lora_finetune_food/lr35_ep10"
+OUTPUT_PATH = "./outputs"
+
+def setup_seeds():
+    seed = 42
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    cudnn.benchmark = False
+    cudnn.deterministic = True
+
+
+def main():
+    download_model(MODEL_PATH)
+    setup_seeds()
+    # logging
+    init_logger(OUTPUT_PATH)
+    # food examples
+    food_examples = load_json(FOOD_EXAMPLES)
+    
+    agent = ConversationalAgent(model_path=MODEL_PATH,
+                                outputs_dir=OUTPUT_PATH)
+    
+    theme = CustomTheme()
+    
+    titles = [
+        """<center><B><font face="Comic Sans MS" size=10>书生大模型实战营</font></B></center>"""  ## Kalam:wght@700
+        """<center><B><font face="Courier" size=5>「进阶岛」InternVL 多模态模型部署微调实践</font></B></center>"""
+    ]
+    
+    language = """Language: 中文 and English"""
+    with gr.Blocks(theme) as demo_chatbot:
+        for title in titles:
+            gr.Markdown(title)
+        # gr.Markdown(article)
+        gr.Markdown(language)
+        
+        with gr.Row():
+            with gr.Column(scale=3):
+                start_btn = gr.Button("Start Chat", variant="primary", interactive=True)
+                clear_btn = gr.Button("Clear Context", interactive=False)
+                image = gr.Image(type="pil", interactive=False)
+                upload_btn = gr.Button("🖼️ Upload Image", interactive=False)
+                
+                with gr.Accordion("Generation Settings"):                    
+                    top_p = gr.Slider(minimum=0, maximum=1, step=0.1,
+                                      value=0.8,
+                                      interactive=True,
+                                      label='top-p value',
+                                      visible=True)
+                    
+                    temperature = gr.Slider(minimum=0, maximum=1.5, step=0.1,
+                                            value=0.8,
+                                            interactive=True,
+                                            label='temperature',
+                                            visible=True)
+                    
+            with gr.Column(scale=7):
+                chat_state = gr.State()
+                chatbot = gr.Chatbot(label='InternVL2', height=800, avatar_images=((os.path.join(os.path.dirname(__file__), 'demo/user.png')), (os.path.join(os.path.dirname(__file__), "demo/bot.png"))))
+                text_input = gr.Textbox(label='User', placeholder="Please click the <Start Chat> button to start chat!", interactive=False)
+                gr.Markdown("### 输入示例")
+                def on_text_change(text):
+                    return gr.update(interactive=True)
+                text_input.change(fn=on_text_change, inputs=text_input, outputs=text_input)
+                gr.Examples(
+                    examples=[["图片中的食物通常属于哪个菜系?"],
+                              ["如果让你简单形容一下品尝图片中的食物的滋味，你会描述它"],
+                              ["去哪个地方游玩时应该品尝当地的特色美食图片中的食物?"],
+                              ["食用图片中的食物时，一般它上菜或摆盘时的特点是?"]],
+                    inputs=[text_input]
+                )
+        
+        with gr.Row():
+            gr.Markdown("### 食物快捷栏")
+        with gr.Row():
+            example_xinjiang_food = gr.Examples(examples=food_examples["新疆菜"], inputs=image, label="新疆菜")
+            example_sichuan_food = gr.Examples(examples=food_examples["川菜（四川，重庆）"], inputs=image, label="川菜（四川，重庆）")
+            example_xibei_food = gr.Examples(examples=food_examples["西北菜 （陕西，甘肃等地）"], inputs=image, label="西北菜 （陕西，甘肃等地）")
+        with gr.Row():
+            example_guizhou_food = gr.Examples(examples=food_examples["黔菜 (贵州）"], inputs=image, label="黔菜 (贵州）")
+            example_jiangsu_food = gr.Examples(examples=food_examples["苏菜（江苏）"], inputs=image, label="苏菜（江苏）")
+            example_guangdong_food = gr.Examples(examples=food_examples["粤菜（广东等地）"], inputs=image, label="粤菜（广东等地）")
+        with gr.Row():
+            example_hunan_food = gr.Examples(examples=food_examples["湘菜（湖南）"], inputs=image, label="湘菜（湖南）")
+            example_fujian_food = gr.Examples(examples=food_examples["闽菜（福建）"], inputs=image, label="闽菜（福建）")
+            example_zhejiang_food = gr.Examples(examples=food_examples["浙菜（浙江）"], inputs=image, label="浙菜（浙江）")
+        with gr.Row():
+            example_dongbei_food = gr.Examples(examples=food_examples["东北菜 （黑龙江等地）"], inputs=image, label="东北菜 （黑龙江等地）")
+            
+                
+        start_btn.click(agent.start_chat, [chat_state], [text_input, start_btn, clear_btn, image, upload_btn, chat_state])
+        clear_btn.click(agent.restart_chat, [chat_state], [chatbot, text_input, start_btn, clear_btn, image, upload_btn, chat_state], queue=False)
+        upload_btn.click(agent.upload_image, [image, chatbot, chat_state], [image, chatbot, chat_state])
+        text_input.submit(
+            agent.respond,
+            inputs=[text_input, image, chatbot, top_p, temperature, chat_state], 
+            outputs=[text_input, image, chatbot, chat_state]
+        )
+
+    demo_chatbot.launch(share=True, server_name="127.0.0.1", server_port=1096, allowed_paths=['./'])
+    demo_chatbot.queue()
+    
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/demo/__init__.py b/demo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..743e4ac650cd862cafd3e1fa880896933afdb26b
--- /dev/null
+++ b/demo/__init__.py
@@ -0,0 +1,8 @@
+from demo.agent import ConversationalAgent
+from demo.theme import CustomTheme
+
+
+__all__ = [
+    "ConversationalAgent",
+    "CustomTheme",
+]
\ No newline at end of file
diff --git a/demo/agent.py b/demo/agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b98b7e9dcbd6e0d2d67cb830031a85ea75c4f89
--- /dev/null
+++ b/demo/agent.py
@@ -0,0 +1,105 @@
+import os
+import logging
+from datetime import datetime
+
+import gradio as gr
+from PIL import Image
+
+from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig, ChatTemplateConfig
+from lmdeploy.vl import load_image
+
+class ConversationalAgent:
+    def __init__(self, 
+                 model_path,
+                 outputs_dir) -> None:
+        self.pipe = pipeline(model_path,
+                        chat_template_config=ChatTemplateConfig(model_name='internvl2-internlm2'),
+                        backend_config=TurbomindEngineConfig(session_len=8192))
+        self.uploaded_images_storage = os.path.join(outputs_dir, "uploaded")
+        self.uploaded_images_storage = os.path.abspath(self.uploaded_images_storage)
+        os.makedirs(self.uploaded_images_storage, exist_ok=True)
+        self.sess = None
+        
+    def start_chat(self, chat_state):
+        self.sess = None
+        self.context = ""
+        self.current_image_id = -1
+        self.image_list = []
+        self.pixel_values_list = []
+        self.seen_image_idx = []
+        logging.info("=" * 30 + "Start Chat" + "=" * 30)
+        
+        return (
+            #gr.update(interactive=False),  # [image] Image
+            gr.update(interactive=True, placeholder='input the text.'),  # [input_text] Textbox
+            gr.update(interactive=False),  # [start_btn] Button
+            gr.update(interactive=True),  # [clear_btn] Button
+            gr.update(interactive=True),  # [image] Image
+            gr.update(interactive=True),  # [upload_btn] Button
+            chat_state  # [chat_state] State
+        )
+        
+    def restart_chat(self, chat_state):
+        self.sess = None
+        self.context = ""
+        self.current_image_id = -1
+        self.image_list = []
+        self.pixel_values_list = []
+        self.seen_image_idx = []
+        
+        logging.info("=" * 30 + "End Chat" + "=" * 30)
+        
+        return (
+            None,  # [chatbot] Chatbot
+            #gr.update(value=None, interactive=True),  # [image] Image
+            gr.update(interactive=False, placeholder="Please click the <Start Chat> button to start chat!"),  # [input_text] Textbox
+            gr.update(interactive=True),  # [start] Button
+            gr.update(interactive=False),  # [clear] Button
+            gr.update(value=None, interactive=False),  # [image] Image
+            gr.update(interactive=False),  # [upload_btn] Button
+            chat_state  # [chat_state] State
+        )
+        
+    def upload_image(self, image: Image.Image, chat_history: gr.Chatbot, chat_state: gr.State):
+        logging.info(f"type(image): {type(image)}")
+        
+        self.image_list.append(image)        
+        save_image_path = os.path.join(self.uploaded_images_storage, "{}.jpg".format(len(os.listdir(self.uploaded_images_storage))))
+        image.save(save_image_path)
+        logging.info(f"image save path: {save_image_path}")
+        chat_history.append((gr.HTML(f'<img src="./file={save_image_path}" style="width: 200px; height: auto; display: inline-block;">'), "Received."))
+        
+        return None, chat_history, chat_state
+    
+    def respond(
+        self,
+        message,
+        image,
+        chat_history: gr.Chatbot,
+        top_p,
+        temperature,
+        chat_state,
+    ):
+        current_time = datetime.now().strftime("%b%d-%H:%M:%S")
+        logging.info(f"Time: {current_time}")
+        logging.info(f"User: {message}")
+        gen_config = GenerationConfig(top_p=top_p, temperature=temperature)
+        chat_input = message
+        if image is not None:
+            save_image_path = os.path.join(self.uploaded_images_storage, "{}.jpg".format(len(os.listdir(self.uploaded_images_storage))))
+            image.save(save_image_path)
+            logging.info(f"image save path: {save_image_path}")
+            chat_input = (message, image)
+        if self.sess is None:
+            self.sess = self.pipe.chat(chat_input, gen_config=gen_config)
+        else:
+            self.sess = self.pipe.chat(chat_input, session=self.sess, gen_config=gen_config)
+        response = self.sess.response.text
+        if image is not None:
+            chat_history.append((gr.HTML(f'{message}\n\n<img src="./file={save_image_path}" style="width: 200px; height: auto; display: inline-block;">'), response))
+        else:
+            chat_history.append((message, response))
+        
+        logging.info(f"generated text = \n{response}")        
+        
+        return "", None, chat_history, chat_state
diff --git a/demo/bot.png b/demo/bot.png
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diff --git a/demo/food_for_demo.json b/demo/food_for_demo.json
new file mode 100644
index 0000000000000000000000000000000000000000..fbe0700703bf12c505d61f070dea629c1b823087
--- /dev/null
+++ b/demo/food_for_demo.json
@@ -0,0 +1,97 @@
+{
+    "新疆菜": [
+        "demo/food/14521898_75_IMG_8637.jpeg",
+        "demo/food/14456664_139_IMG_0917-EDIT.jpg",
+        "demo/food/14456664_80_IMG_6323.jpeg",
+        "demo/food/14456664_217_IMG_3854.jpeg",
+        "demo/food/14521898_14_IMG_3852.jpeg",
+        "demo/food/14456664_136_IMG_20220709_233905.jpg",
+        "demo/food/14456664_137_IMG_20220717_143941.jpg",
+        "demo/food/14456664_141_IMG_1724.jpg",
+        "demo/food/14456664_134_IMG_20220709_153652.jpg",
+        "demo/food/14456664_133_IMG_20220709_153436.jpg"
+    ],
+    "川菜（四川，重庆）": [
+        "demo/food/14521898_113_IMG_1724.jpeg",
+        "demo/food/14521898_193_IMG_2435.jpeg",
+        "demo/food/14521898_44_930F3702-FC28-498F-A5A7-9E01AA5AEA15.jpeg",
+        "demo/food/14521898_80_IMG_2613.jpeg",
+        "demo/food/14456664_68_IMG_0971.jpeg",
+        "demo/food/14521898_174_IMG_8019.jpeg",
+        "demo/food/14456664_88_wx_camera_1712730236546.jpg",
+        "demo/food/14456664_36_20240410160819.jpg",
+        "demo/food/14456664_238_d370e16ff482d13a0718c6fb4cd7ffbb.jpeg",
+        "demo/food/14521898_183_IMG_20240502_122921.jpg",
+        "demo/food/14456664_40_IMG_20170613_190017.jpg",
+        "demo/food/14456664_150_IMG_20181218_190852.jpg",
+        "demo/food/14456664_72_IMG_0618.jpeg",
+        "demo/food/14521898_82_IMG_9847.jpeg"
+    ],
+    "西北菜 （陕西，甘肃等地）": [
+        "demo/food/14456664_82_MVIMG_20240413_161454.jpeg",
+        "demo/food/14521898_261_0E3192C7-1B0D-4C4D-9788-5E7A5E6E92BA.jpeg",
+        "demo/food/14456664_50_mmexport1712821641752.jpg",
+        "demo/food/14456664_126_86838F28-912B-42A4-80C1-BD060B649081.jpeg",
+        "demo/food/14521898_262_75B3AEDF-C705-400B-AE86-3FA7A92B624C.jpeg",
+        "demo/food/14456664_22_IMG_20240404_135935.jpg",
+        "demo/food/14521898_265_1C4A3265-A18E-46C2-A264-4384B85A49C0.jpeg",
+        "demo/food/14456664_127_1E703639-9169-4EB4-8A5D-3B1E226F639B.jpeg"
+    ],
+    "黔菜 (贵州）": [
+        "demo/food/14521898_35_u_23162001_2841880362fm_170s_80187E975C411EC042A.png",
+        "demo/food/14456664_61_mmexport1550554859117.jpg",
+        "demo/food/14456664_201_mmexport1713452408322.jpg",
+        "demo/food/14521898_211_IMG_9139.jpeg",
+        "demo/food/14456664_187_mmexport1713446177339.jpg",
+        "demo/food/14521898_97_Image_1713865926721.jpg",
+        "demo/food/14456664_41_IMG_20190216_124017.jpeg",
+        "demo/food/14521898_93_Image_1713865938858.jpg"
+    ],
+    "苏菜（江苏）": [
+        "demo/food/14456664_64_IMG_20240409_181047.jpg",
+        "demo/food/14456664_149_IMG_20180812_182400.jpg",
+        "demo/food/14521898_65_IMG_20240424_121225.jpg",
+        "demo/food/14456664_236_IMG_4742.jpeg",
+        "demo/food/14521898_55_IMG_8533.jpeg",
+        "demo/food/14521898_109_IMG_5997.jpeg",
+        "demo/food/14521898_79_IMG_2612.jpeg",
+        "demo/food/14456664_34_IMG_0400.jpeg",
+        "demo/food/14521898_248_D8768DE2-559E-4850-A95E-07D5539F06C1.jpeg"
+    ],
+    "粤菜（广东等地）": [
+        "demo/food/14521898_257_79874AA6-4F23-4AD6-96E2-7F64039A81C0.jpeg",
+        "demo/food/14521898_144_IMG_8800.jpeg",
+        "demo/food/14456664_155_IMG_20240403_094547.jpg",
+        "demo/food/14456664_87_IMG_5316.jpeg",
+        "demo/food/14521898_249_0755477B-B9D5-4786-8D86-BFD4AC9478FD.jpeg",
+        "demo/food/14521898_112_IMG_3570.jpeg",
+        "demo/food/14456664_153_IMG_20240411_102841.jpg",
+        "demo/food/14456664_70_IMG_0581.jpeg",
+        "demo/food/14521898_162_IMG_0356.jpeg",
+        "demo/food/14521898_117_IMG_1362.jpeg"
+    ],
+    "湘菜（湖南）": [
+        "demo/food/14521898_12_fb48488412a1846c104b28600f4f1ded.jpeg",
+        "demo/food/14456664_147_IMG_20190225_184723.jpg",
+        "demo/food/14521898_209_IMG_3105.jpeg"
+    ],
+    "闽菜（福建）": [
+        "demo/food/14521898_108_IMG_6205.jpeg",
+        "demo/food/14456664_129_IMG_20220605_181308.jpg",
+        "demo/food/14521898_168_D4D36C59-2D68-4354-B05C-B3065F6581AA.jpeg"
+    ],
+    "浙菜（浙江）": [
+        "demo/food/14456664_159_IMG_20240416_140243.jpg",
+        "demo/food/14456664_239_mmexport1713518196678.jpg",
+        "demo/food/14456664_130_IMG_20220605_180820.jpg",
+        "demo/food/14456664_117_IMG_8176.jpeg",
+        "demo/food/14456664_19_IMG_0395.jpeg",
+        "demo/food/14456664_158_IMG_20240416_141427.jpg",
+        "demo/food/14521898_122_53D0E884-E012-4FFA-8BD7-8F5666A53123.jpeg"
+    ],
+    "东北菜 （黑龙江等地）": [
+        "demo/food/14456664_26_IMG_3472.jpeg",
+        "demo/food/14521898_237_IMG_5909.jpeg",
+        "demo/food/14456664_14_IMG_20220721_150241.jpg"
+    ]
+}
\ No newline at end of file
diff --git a/demo/theme.py b/demo/theme.py
new file mode 100644
index 0000000000000000000000000000000000000000..3530a4b7e9427541e4613d83b16db0bef0414ca3
--- /dev/null
+++ b/demo/theme.py
@@ -0,0 +1,53 @@
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+from typing import Union, Iterable
+
+
+class CustomTheme(Base):
+    def __init__(
+        self,
+        primary_hue: Union[colors.Color, str] = colors.emerald,
+        secondary_hue: Union[colors.Color, str] = colors.blue,
+        neutral_hue: Union[colors.Color, str] = colors.slate,
+        spacing_size: Union[sizes.Size, str] = sizes.spacing_md,
+        radius_size: Union[sizes.Size, str] = sizes.radius_md,
+        text_size: Union[sizes.Size, str] = sizes.text_lg,
+        font: Union[fonts.Font, str, Iterable[Union[fonts.Font, str]]] = (
+            fonts.GoogleFont("Alice"),
+            "ui-sans-serif",
+            "sans-serif",
+        ),
+        font_mono: Union[fonts.Font, str, Iterable[Union[fonts.Font, str]]] = (
+            fonts.GoogleFont("Merriweather"),
+            "ui-monospace",
+            "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            spacing_size=spacing_size,
+            radius_size=radius_size,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            body_background_fill="#ECF2F7",
+            body_background_fill_dark="#191919",
+            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
+            button_primary_background_fill_hover="*primary_700",
+            button_primary_text_color="white",
+            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
+            slider_color="#4EACEF",
+            slider_color_dark="#4EACEF",
+            block_title_text_weight="600",
+            block_title_text_size="*text_md",
+            block_label_text_weight="600",
+            block_label_text_size="*text_md",
+            block_border_width="1px",
+            block_shadow="#FFFFFF00",
+            button_shadow="*shadow_drop_lg",
+            button_large_padding="*spacing_lg calc(2 * *spacing_lg)",
+        )
\ No newline at end of file
diff --git a/demo/user.png b/demo/user.png
new file mode 100644
index 0000000000000000000000000000000000000000..5dfdd89145ba278a582e454d011b524c2696e0ab
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diff --git a/download.py b/download.py
new file mode 100644
index 0000000000000000000000000000000000000000..a02938ebe0f35045a15c2e132319fb20e0b49f00
--- /dev/null
+++ b/download.py
@@ -0,0 +1,9 @@
+from huggingface_hub import hf_hub_download
+import joblib
+
+REPO_ID = "KitHung/InternVL_food_lr35_ep10"
+
+def download_model(model_path):
+    joblib.load(
+        hf_hub_download(repo_id=REPO_ID, filename=model_path)
+    )
\ No newline at end of file
diff --git a/process_food.py b/process_food.py
new file mode 100644
index 0000000000000000000000000000000000000000..6acd53da37f76a7f9f34765d72b80290d2abbbf0
--- /dev/null
+++ b/process_food.py
@@ -0,0 +1,25 @@
+import json
+input_path = "/root/huggingface/FoodieQA/FoodieQA/sivqa_tidy.json"  # sivqa_tidy.json所在位置
+output_path = "/root/huggingface/FoodieQA/FoodieQA/sivqa_llava.json"  # 输出文件位置
+
+with open(input_path, 'r', encoding='utf-8') as f:
+    foodqa = json.load(f)
+
+llava_format = []
+for data in foodqa:
+    llava_format.append({
+        "image": data['food_meta']['food_file'],
+        "conversations": [
+            {
+                "from": "human",
+                "value": data['question']+"\n<image>"
+            },
+            {
+                "from": "gpt",
+                "value": data['choices'][int(data['answer'])] + "，图中的菜是"+ data['food_meta']['food_name']
+            }
+        ]
+    })
+
+with open(output_path, 'w', encoding='utf-8') as f:
+    json.dump(llava_format, f, indent=4, ensure_ascii=False)
\ No newline at end of file
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..be4d578eb65559e4d083d351ae24533def9d05af
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,25 @@
+import os
+import json
+import logging
+from datetime import datetime
+
+
+def load_json(file_name: str):
+    if isinstance(file_name, str) and file_name.endswith("json"):
+        with open(file_name, 'r') as file:
+            data = json.load(file)
+    else:
+        raise ValueError("The file path you passed in is not a json file path.")
+    
+    return data
+
+def init_logger(outputs_dir):
+    current_time = datetime.now().strftime("%b%d_%H-%M-%S")
+    os.makedirs(os.path.join(outputs_dir, "logs"), exist_ok=True)
+    log_path = os.path.join(outputs_dir, "logs", "{}.txt".format(current_time))
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s || %(message)s",
+        handlers=[logging.StreamHandler(), logging.FileHandler(log_path)],
+    )
+    
\ No newline at end of file
diff --git a/xtuner_config/internvl_v2_internlm2_2b_lora_finetune_food.py b/xtuner_config/internvl_v2_internlm2_2b_lora_finetune_food.py
new file mode 100644
index 0000000000000000000000000000000000000000..43af705e5baa4af43ca1397b200aab53b6fef986
--- /dev/null
+++ b/xtuner_config/internvl_v2_internlm2_2b_lora_finetune_food.py
@@ -0,0 +1,184 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
+from peft import LoraConfig
+from torch.optim import AdamW
+from transformers import AutoTokenizer
+
+from xtuner.dataset import InternVL_V1_5_Dataset
+from xtuner.dataset.collate_fns import default_collate_fn
+from xtuner.dataset.samplers import LengthGroupedSampler
+from xtuner.engine.hooks import DatasetInfoHook
+from xtuner.engine.runner import TrainLoop
+from xtuner.model import InternVL_V1_5
+from xtuner.utils import PROMPT_TEMPLATE
+
+#######################################################################
+#                          PART 1  Settings                           #
+#######################################################################
+# Model
+path = '/root/share/new_models/OpenGVLab/InternVL2-2B'
+
+# Data
+data_root = '/root/share/datasets/FoodieQA/'  # your data path
+data_path = data_root + 'sivqa_llava.json'
+image_folder = data_root  # your image folder path
+prompt_template = PROMPT_TEMPLATE.internlm2_chat
+max_length = 8192
+
+# Scheduler & Optimizer
+batch_size = 4  # per_device
+accumulative_counts = 2
+dataloader_num_workers = 4
+max_epochs = 10
+optim_type = AdamW
+# official 1024 -> 4e-5
+# lr = 1e-6
+lr = 3e-5
+betas = (0.9, 0.999)
+weight_decay = 0.05
+max_norm = 1  # grad clip
+warmup_ratio = 0.03
+
+# Save
+save_steps = 64
+save_total_limit = -1  # Maximum checkpoints to keep (-1 means unlimited)
+
+#######################################################################
+#            PART 2  Model & Tokenizer & Image Processor              #
+#######################################################################
+model = dict(
+    type=InternVL_V1_5,
+    model_path=path,
+    freeze_llm=True,
+    freeze_visual_encoder=True,
+    # comment the following lines if you don't want to use Lora in llm
+    llm_lora=dict(
+        type=LoraConfig,
+        r=128,
+        lora_alpha=256,
+        lora_dropout=0.05,
+        target_modules=None,
+        task_type='CAUSAL_LM'),
+    # uncomment the following lines if you don't want to use Lora in visual encoder # noqa
+    # visual_encoder_lora=dict(
+    #     type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05,
+    #     target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'])
+)
+
+#######################################################################
+#                      PART 3  Dataset & Dataloader                   #
+#######################################################################
+llava_dataset = dict(
+    type=InternVL_V1_5_Dataset,
+    model_path=path,
+    data_paths=data_path,
+    image_folders=image_folder,
+    template=prompt_template,
+    max_length=max_length)
+
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=dataloader_num_workers,
+    dataset=llava_dataset,
+    sampler=dict(
+        type=LengthGroupedSampler,
+        length_property='modality_length',
+        per_device_batch_size=batch_size * accumulative_counts),
+    collate_fn=dict(type=default_collate_fn))
+
+#######################################################################
+#                    PART 4  Scheduler & Optimizer                    #
+#######################################################################
+# optimizer
+optim_wrapper = dict(
+    type=AmpOptimWrapper,
+    optimizer=dict(
+        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
+    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
+    accumulative_counts=accumulative_counts,
+    loss_scale='dynamic',
+    dtype='float16')
+
+# learning policy
+# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
+param_scheduler = [
+    dict(
+        type=LinearLR,
+        start_factor=1e-5,
+        by_epoch=True,
+        begin=0,
+        end=warmup_ratio * max_epochs,
+        convert_to_iter_based=True),
+    dict(
+        type=CosineAnnealingLR,
+        eta_min=0.0,
+        by_epoch=True,
+        begin=warmup_ratio * max_epochs,
+        end=max_epochs,
+        convert_to_iter_based=True)
+]
+
+# train, val, test setting
+train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
+
+#######################################################################
+#                           PART 5  Runtime                           #
+#######################################################################
+# Log the dialogue periodically during the training process, optional
+tokenizer = dict(
+    type=AutoTokenizer.from_pretrained,
+    pretrained_model_name_or_path=path,
+    trust_remote_code=True)
+
+custom_hooks = [
+    dict(type=DatasetInfoHook, tokenizer=tokenizer),
+]
+
+# configure default hooks
+default_hooks = dict(
+    # record the time of every iteration.
+    timer=dict(type=IterTimerHook),
+    # print log every 10 iterations.
+    logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
+    # enable the parameter scheduler.
+    param_scheduler=dict(type=ParamSchedulerHook),
+    # save checkpoint per `save_steps`.
+    checkpoint=dict(
+        type=CheckpointHook,
+        save_optimizer=False,
+        by_epoch=False,
+        interval=save_steps,
+        max_keep_ckpts=save_total_limit),
+    # set sampler seed in distributed evrionment.
+    sampler_seed=dict(type=DistSamplerSeedHook),
+)
+
+# configure environment
+env_cfg = dict(
+    # whether to enable cudnn benchmark
+    cudnn_benchmark=False,
+    # set multi process parameters
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    # set distributed parameters
+    dist_cfg=dict(backend='nccl'),
+)
+
+# set visualizer
+visualizer = None
+
+# set log level
+log_level = 'INFO'
+
+# load from which checkpoint
+load_from = None
+
+# whether to resume training from the loaded checkpoint
+resume = False
+
+# Defaults to use random seed and disable `deterministic`
+randomness = dict(seed=None, deterministic=False)
+
+# set log processor
+log_processor = dict(by_epoch=False)