diff --git a/lmms_eval/models/model_utils/load_video.py b/lmms_eval/models/model_utils/load_video.py
index 0c4ea23d..2d4879cc 100644
--- a/lmms_eval/models/model_utils/load_video.py
+++ b/lmms_eval/models/model_utils/load_video.py
@@ -1,6 +1,19 @@
 import av
 import numpy as np
 from av.codec.context import CodecContext
+from decord import VideoReader, cpu
+
+
+def load_video_decord(video_path, max_frames_num):
+    if type(video_path) == str:
+        vr = VideoReader(video_path, ctx=cpu(0))
+    else:
+        vr = VideoReader(video_path[0], ctx=cpu(0))
+    total_frame_num = len(vr)
+    uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
+    frame_idx = uniform_sampled_frames.tolist()
+    spare_frames = vr.get_batch(frame_idx).asnumpy()
+    return spare_frames  # (frames, height, width, channels)
 
 
 # This one is faster
diff --git a/lmms_eval/models/qwen2_vl.py b/lmms_eval/models/qwen2_vl.py
index 9848f911..565b7888 100755
--- a/lmms_eval/models/qwen2_vl.py
+++ b/lmms_eval/models/qwen2_vl.py
@@ -3,6 +3,7 @@
 from typing import List, Optional, Tuple, Union
 
 import decord
+import numpy as np
 import torch
 from accelerate import Accelerator, DistributedType
 from loguru import logger as eval_logger
@@ -14,6 +15,7 @@
 from lmms_eval.api.instance import Instance
 from lmms_eval.api.model import lmms
 from lmms_eval.api.registry import register_model
+from lmms_eval.models.model_utils.load_video import load_video_decord
 
 try:
     from qwen_vl_utils import process_vision_info
@@ -35,7 +37,10 @@ def __init__(
         device_map: Optional[str] = "cuda",
         batch_size: Optional[Union[int, str]] = 1,
         use_cache=True,
-        use_flash_attention_2: Optional[bool] = True,
+        use_flash_attention_2: Optional[bool] = False,
+        max_pixels: int = 12845056,
+        min_pixels: int = 3136,
+        max_num_frames: int = 32,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -62,7 +67,10 @@ def __init__(
             ).eval()
         else:
             self._model = Qwen2VLForConditionalGeneration.from_pretrained(pretrained, torch_dtype="auto", device_map=self.device_map).eval()
-        self.processor = AutoProcessor.from_pretrained(pretrained)
+        self.processor = AutoProcessor.from_pretrained(pretrained, max_pixels=max_pixels, min_pixels=min_pixels)
+        self.max_pixels = max_pixels
+        self.min_pixels = min_pixels
+        self.max_num_frames = max_num_frames
         self._tokenizer = AutoTokenizer.from_pretrained(pretrained)
 
         self._config = self.model.config
@@ -198,8 +206,8 @@ def _collate(x):
                         vr = decord.VideoReader(visual)
                         first_frame = vr[0].asnumpy()
                         height, width = first_frame.shape[:2]
-                        max_pixels = height * width
-                        message.append({"role": "user", "content": [{"type": "video", "video": visual, "max_pixels": max_pixels}, {"type": "text", "text": context}]})
+                        # max_pixels = height * width
+                        message.append({"role": "user", "content": [{"type": "video", "video": visual, "max_pixels": self.max_pixels}, {"type": "text", "text": context}]})
                     elif isinstance(visual, Image.Image):  # Single image
                         base64_image = visual.convert("RGB")
                         buffer = BytesIO()
@@ -226,6 +234,12 @@ def _collate(x):
 
             texts = [self.processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages]
             image_inputs, video_inputs = process_vision_info(messages)
+            total_frames = video_inputs[0].shape[0]
+            indices = np.linspace(0, total_frames - 1, self.max_num_frames, dtype=int)
+            # Append the last frame index if not already included
+            if total_frames - 1 not in indices:
+                indices = np.append(indices, total_frames - 1)
+            video_inputs[0] = video_inputs[0][indices]
             inputs = self.processor(text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt")
 
             if self.device_map == "auto":