diff --git a/PyTorch/built-in/mlm/PLLaVA/README.md b/PyTorch/built-in/mlm/PLLaVA/README.md
index a856ec3fc38e9b84fdcec70cdd3bb048559f4356..c4734f0daada9dc9f0c7410bbcae3b115e090f73 100644
--- a/PyTorch/built-in/mlm/PLLaVA/README.md
+++ b/PyTorch/built-in/mlm/PLLaVA/README.md
@@ -61,7 +61,7 @@ PLLaVA是一种新颖的端到端训练的大型多模态模型，它结合了
 
 - 创建Python环境并且安装Python三方包：
   ```shell
-  conda create -n llava python=3.10 -y
+  conda create -n pllava python=3.10 -y
   conda activate pllava
   pip install --upgrade pip  # enable PEP 660 support
   pip3 install torch==2.1.0+cpu  --index-url https://download.pytorch.org/whl/cpu  #For X86
@@ -122,10 +122,12 @@ PLLaVA是一种新颖的端到端训练的大型多模态模型，它结合了
 
 **表 2**  训练结果展示：
 
-|      芯片       | 卡数 | second per step | batch_size | AMP_Type | Torch_Version |
-|:-------------:|:---:|:---------------:|:----------:|:---:|:---:|
-|      竞品A      | 8p |      0.84s      |     1      | bf16 | 2.1 |
-| Atlas 800T A2 | 8p |      0.85s      |     1      | bf16 | 2.1 |
+|         芯片         | 卡数 | second per step | batch_size | AMP_Type | Torch_Version |
+|:------------------:|:---:|:---------------:|:----------:|:--------:|:---:|
+|        竞品A         | 8p |     0.9352s     |     1      |   bf16   | 2.1 |
+| Atlas 200 A2 Box16 | 8p |     0.8411s     |     1      |   bf16   | 2.1 |
+|        竞品A         | 8p |     1.0760s     |     1      |   fp32   | 2.1 |
+|   Atlas 200 A2 Box16    | 8p |     0.9347s     |     1      |   fp32   | 2.1 |
 
 
 ## 模型推理
diff --git a/PyTorch/built-in/mlm/PLLaVA/requirements.txt b/PyTorch/built-in/mlm/PLLaVA/requirements.txt
index 6feac297633bfd29eb3a6b0970e9aaad456a2603..48218321d8f5b0731f386e3c36d7ed8a16a74dac 100644
--- a/PyTorch/built-in/mlm/PLLaVA/requirements.txt
+++ b/PyTorch/built-in/mlm/PLLaVA/requirements.txt
@@ -72,7 +72,7 @@ hjson==3.1.0
 hpack==4.0.0
 httpcore==1.0.4
 httpx==0.27.0
-huggingface-hub==0.21.4
+huggingface-hub==0.24.5
 humanize==4.9.0
 hupper==1.12.1
 Hypercorn==0.16.0
@@ -204,7 +204,7 @@ texttable==1.7.0
 threadpoolctl==3.3.0
 tifffile==2024.2.12
 timm==0.6.12
-tokenizers==0.15.2
+tokenizers==0.19.1
 tomli==2.0.1
 tomlkit==0.12.0
 toolz==0.12.1
diff --git a/PyTorch/built-in/mlm/PLLaVA/scripts/eval_single.sh b/PyTorch/built-in/mlm/PLLaVA/scripts/eval_single.sh
index 4aeaab90ea59f3625aaecdd21f2bd4ecf3b00bae..ae3d957c437b25b67be371ccff32787e1a3be364 100644
--- a/PyTorch/built-in/mlm/PLLaVA/scripts/eval_single.sh
+++ b/PyTorch/built-in/mlm/PLLaVA/scripts/eval_single.sh
@@ -12,7 +12,7 @@ lora_alpha=4
 video_path=/path_to_PLLaVA/example/cooking.mp4
 
 conv_mode=eval_videoqabench
-python -m tasks.eval.videoqabench.pllava_eval_videoqabench \
+python -m tasks.eval.videoqabench.pllava_eval_single \
     --pretrained_model_name_or_path ${model_dir} \
     --save_path ${SAVE_DIR}/videoqabench \
     --num_frames ${num_frames} \
diff --git a/PyTorch/built-in/mlm/PLLaVA/tasks/eval/videoqabench/pllava_eval_single.py b/PyTorch/built-in/mlm/PLLaVA/tasks/eval/videoqabench/pllava_eval_single.py
index 59872424304392be192e476cb0ebe26c4c6a3ad6..a6778c73db67fd95860896440951cae9f9875226 100644
--- a/PyTorch/built-in/mlm/PLLaVA/tasks/eval/videoqabench/pllava_eval_single.py
+++ b/PyTorch/built-in/mlm/PLLaVA/tasks/eval/videoqabench/pllava_eval_single.py
@@ -21,6 +21,8 @@ from decord import VideoReader, cpu
 from tasks.eval.model_utils import load_pllava, pllava_answer
 from tasks.eval.eval_utils import conv_templates
 
+from torch import nn, Tensor
+
 logging.basicConfig()
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -165,6 +167,21 @@ def single_test(model, processor, vid_path, num_frames=4, conv_mode="plain", eva
         conv.user_query("Describe the video in details.", is_mm=True)
     llm_response, conv = pllava_answer(conv=conv, model=model, processor=processor, do_sample=False, img_list=img_list, max_new_tokens=256, print_res=True)
 
+def adaptive_avg_pool3d(input: Tensor, output_size) -> Tensor:
+    input_dtype = input.dtype
+    input_shape = input.shape
+    input = input.to(dtype=torch.float32)
+    pool = nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, ceil_mode=False)
+    output = pool(
+        input.reshape(input_shape[0] * input_shape[1], input_shape[2], input_shape[3], input_shape[4])).reshape(
+        input_shape[0], input_shape[1], output_size[0], output_size[1], output_size[2])
+    output = output.to(dtype=input_dtype)
+    return output
+
+def replace_with_adaptive_avg_pool3d():
+    torch.nn.functional.adaptive_avg_pool3d = adaptive_avg_pool3d
+
+
 def main():
     multiprocess=True
     mp.set_start_method('spawn',force=True)
@@ -189,4 +206,5 @@ def main():
     logger.info('single test done...')
 
 if __name__ == "__main__":
+    replace_with_adaptive_avg_pool3d()
     main()
\ No newline at end of file
diff --git a/PyTorch/built-in/mlm/PLLaVA/utils/basic_utils.py b/PyTorch/built-in/mlm/PLLaVA/utils/basic_utils.py
index d2ca1ca21193decacead3e71f9d7bd45a77f45c4..0bd8f173a14818d74d866be5bb1d8c9cb2c60fe4 100644
--- a/PyTorch/built-in/mlm/PLLaVA/utils/basic_utils.py
+++ b/PyTorch/built-in/mlm/PLLaVA/utils/basic_utils.py
@@ -185,8 +185,8 @@ class MetricLogger(object):
             end = time.time()
         total_time = time.time() - start_time
         total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        logger.info('{} Total time: {} ({:.4f} s / it)'.format(
-            header, total_time_str, total_time / len(iterable)))
+        logger.info('Total time: {}'.format(total_time_str))
+        logger.info('Averaged time: {:.4f}  s / it'.format(total_time / len(iterable)))
 
 
 class AttrDict(dict):