diff --git a/scripts/internvl2_s b/scripts/internvl2_s
new file mode 100644
index 0000000..0a8df30
--- /dev/null
+++ b/scripts/internvl2_s
@@ -0,0 +1,247 @@
+import json
+from argparse import ArgumentParser
+import torch
+import os
+import json
+from tqdm import tqdm
+from PIL import Image
+import math
+import multiprocessing
+from multiprocessing import Pool, Queue, Manager
+
+from transformers import AutoModelForCausalLM, AutoTokenizer,AutoModel
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+# https://huggingface.co/OpenGVLab/InternVL2-1B
+
+def split_list(lst, n):
+    length = len(lst)
+    avg = length // n  # 每份的大小
+    result = []  # 存储分割后的子列表
+    for i in range(n - 1):
+        result.append(lst[i*avg:(i+1)*avg])
+    result.append(lst[(n-1)*avg:])
+    return result
+
+def save_json(json_list,save_path):
+    with open(save_path, 'w') as file:
+        json.dump(json_list, file,indent=4)
+
+def _get_args():
+    parser = ArgumentParser()
+    parser.add_argument("--image_folder", type=str, default="./OCRBench_Images")
+    parser.add_argument("--output_folder", type=str, default="./results")
+    parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
+    parser.add_argument("--model_path", type=str, default='OpenGVLab/InternVL2-4B')
+    parser.add_argument("--save_name", type=str, default="internvl2-4B")
+    parser.add_argument("--num_workers", type=int, default=1)
+    args = parser.parse_args()
+    return args
+
+OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0,
+"Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,
+"Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0}
+AllDataset_score = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
+"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
+num_all = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
+"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
+
+def eval_worker(args, data, eval_id, output_queue):
+    print(f"Process {eval_id} start.")
+    checkpoint = args.model_path
+    model = AutoModel.from_pretrained(
+        checkpoint,
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True).eval().to(f'cuda:{eval_id}')
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True, use_fast=False)
+
+    for i in tqdm(range(len(data))):
+        img_path = os.path.join(args.image_folder, data[i]['image_path'])
+        qs = data[i]['question']
+        pixel_values = load_image(img_path, max_num=12).to(torch.bfloat16).to(f'cuda:{eval_id}')
+        generation_config = dict(max_new_tokens=1024, do_sample=False)
+        question = f'<image>\n{qs}'
+        response = model.chat(tokenizer, pixel_values, question, generation_config)
+        data[i]['predict'] = response
+    output_queue.put({eval_id: data})
+    print(f"Process {eval_id} has completed.")
+
+if __name__=="__main__":
+    multiprocessing.set_start_method('spawn')
+    args = _get_args()
+    
+    if os.path.exists(os.path.join(args.output_folder,f"{args.save_name}.json")):
+        data_path = os.path.join(args.output_folder,f"{args.save_name}.json")
+        print(f"output_path:{data_path} exist! Only generate the results that were not generated in {data_path}.")
+    else:
+        data_path = args.OCRBench_file
+
+    with open(data_path, "r") as f:
+        data = json.load(f)
+    
+    data_list = split_list(data, args.num_workers)
+
+    output_queue = Manager().Queue()
+
+    pool = Pool(processes=args.num_workers)
+    for i in range(len(data_list)):
+        pool.apply_async(eval_worker, args=(args, data_list[i], i, output_queue))
+    pool.close()
+    pool.join()
+    # eval_worker(args, data_list[0], 0, output_queue)
+
+    results = {}
+    while not output_queue.empty():
+        result = output_queue.get()
+        results.update(result)
+    data = []
+    for i in range(len(data_list)):
+        data.extend(results[i])
+
+    for i in range(len(data)):
+        data_type = data[i]["type"]
+        dataset_name = data[i]["dataset_name"]
+        answers = data[i]["answers"]
+        if data[i].get('predict',0)==0:
+            continue
+        predict = data[i]['predict']
+        data[i]['result'] = 0
+        if dataset_name == "HME100k":
+            if type(answers)==list:
+                for j in range(len(answers)):
+                    answer = answers[j].strip().replace("\n"," ").replace(" ","")
+                    predict = predict.strip().replace("\n"," ").replace(" ","")
+                    if answer in predict:
+                        data[i]['result'] = 1
+            else:
+                answers = answers.strip().replace("\n"," ").replace(" ","")
+                predict = predict.strip().replace("\n"," ").replace(" ","")
+                if answers in predict:
+                    data[i]['result'] = 1
+        else:
+            if type(answers)==list:
+                for j in range(len(answers)):
+                    answer = answers[j].lower().strip().replace("\n"," ")
+                    predict = predict.lower().strip().replace("\n"," ")
+                    if answer in predict:
+                        data[i]['result'] = 1
+            else:
+                answers = answers.lower().strip().replace("\n"," ")
+                predict = predict.lower().strip().replace("\n"," ")
+                if answers in predict:
+                    data[i]['result'] = 1
+    save_json(data, os.path.join(args.output_folder,f"{args.save_name}.json"))
+    if len(data)==1000:
+        for i in range(len(data)):
+            if data[i].get("result",100)==100:
+                continue
+            OCRBench_score[data[i]['type']] += data[i]['result']
+        recognition_score = OCRBench_score['Regular Text Recognition']+OCRBench_score['Irregular Text Recognition']+OCRBench_score['Artistic Text Recognition']+OCRBench_score['Handwriting Recognition']+OCRBench_score['Digit String Recognition']+OCRBench_score['Non-Semantic Text Recognition']
+        Final_score = recognition_score+OCRBench_score['Scene Text-centric VQA']+OCRBench_score['Doc-oriented VQA']+OCRBench_score['Key Information Extraction']+OCRBench_score['Handwritten Mathematical Expression Recognition']
+        print("###########################OCRBench##############################")
+        print(f"Text Recognition(Total 300):{recognition_score}")
+        print("------------------Details of Recognition Score-------------------")
+        print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}")
+        print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}")
+        print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}")
+        print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}")
+        print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}")
+        print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}")
+        print("----------------------------------------------------------------")
+        print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}")
+        print("----------------------------------------------------------------")
+        print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}")
+        print("----------------------------------------------------------------")
+        print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}")
+        print("----------------------------------------------------------------")
+        print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}")
+        print("----------------------Final Score-------------------------------")
+        print(f"Final Score(Total 1000): {Final_score}")
+    else:
+        for i in range(len(data)):
+            num_all[data[i]['dataset_name']] += 1
+            if data[i].get("result",100)==100:
+                continue
+            AllDataset_score[data[i]['dataset_name']] += data[i]['result']
+        for key in AllDataset_score.keys():
+            print(f"{key}: {AllDataset_score[key]/float(num_all[key])}")