diff --git a/inference/smolvlm/SmolVLM_video_inference.py b/inference/smolvlm/SmolVLM_video_inference.py
new file mode 100644
index 0000000..39d62ff
--- /dev/null
+++ b/inference/smolvlm/SmolVLM_video_inference.py
@@ -0,0 +1,156 @@
+import torch
+from transformers import AutoProcessor, Idefics3ForConditionalGeneration
+from PIL import Image
+import cv2
+import numpy as np
+from typing import List
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class VideoFrameExtractor:
+    def __init__(self, max_frames: int = 50):
+        self.max_frames = max_frames
+        
+    def resize_and_center_crop(self, image: Image.Image, target_size: int) -> Image.Image:
+        # Get current dimensions
+        width, height = image.size
+        
+        # Calculate new dimensions keeping aspect ratio
+        if width < height:
+            new_width = target_size
+            new_height = int(height * (target_size / width))
+        else:
+            new_height = target_size
+            new_width = int(width * (target_size / height))
+            
+        # Resize
+        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+        
+        # Center crop
+        left = (new_width - target_size) // 2
+        top = (new_height - target_size) // 2
+        right = left + target_size
+        bottom = top + target_size
+        
+        return image.crop((left, top, right, bottom))
+        
+    def extract_frames(self, video_path: str) -> List[Image.Image]:
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            raise ValueError(f"Could not open video: {video_path}")
+            
+        # Get video properties
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        fps = int(cap.get(cv2.CAP_PROP_FPS))
+        
+        # Calculate frame indices to extract (1fps)
+        frame_indices = list(range(0, total_frames, fps))
+        
+        # If we have more frames than max_frames, sample evenly
+        if len(frame_indices) > self.max_frames:
+            indices = np.linspace(0, len(frame_indices) - 1, self.max_frames, dtype=int)
+            frame_indices = [frame_indices[i] for i in indices]
+        
+        frames = []
+        for frame_idx in frame_indices:
+            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+            ret, frame = cap.read()
+            if ret:
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                pil_image = Image.fromarray(frame)
+                pil_image = self.resize_and_center_crop(pil_image, 384)
+                frames.append(pil_image)
+        
+        cap.release()
+        return frames
+
+def load_model(checkpoint_path: str, base_model_id: str = "HuggingFaceTB/SmolVLM-Instruct", device: str = "cuda"):
+    # Load processor from original model
+    processor = AutoProcessor.from_pretrained(base_model_id)
+    if checkpoint_path:
+        # Load fine-tuned model from checkpoint
+        model = Idefics3ForConditionalGeneration.from_pretrained(
+            checkpoint_path,
+            torch_dtype=torch.bfloat16,
+            device_map=device
+        )
+    else:
+        model = Idefics3ForConditionalGeneration.from_pretrained(
+            base_model_id,
+            torch_dtype=torch.bfloat16,
+            device_map=device
+        )    
+
+    # Configure processor for video frames
+    processor.image_processor.size = (384, 384)
+    processor.image_processor.do_resize = False
+    processor.image_processor.do_image_splitting = False
+    
+    return model, processor
+
+def generate_response(model, processor, video_path: str, question: str, max_frames: int = 50):
+    # Extract frames
+    frame_extractor = VideoFrameExtractor(max_frames)
+    frames = frame_extractor.extract_frames(video_path)
+    logger.info(f"Extracted {len(frames)} frames from video")
+    
+    # Create prompt with frames
+    image_tokens = [{"type": "image"} for _ in range(len(frames))]
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Answer briefly."},
+                *image_tokens,
+                {"type": "text", "text": question}
+            ]
+        }
+    ]
+
+    # Process inputs
+    inputs = processor(
+        text=processor.apply_chat_template(messages, add_generation_prompt=True),
+        images=[img for img in frames],
+        return_tensors="pt"
+    ).to(model.device)
+
+    # Generate response
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        num_beams=5,
+        temperature=0.7,
+        do_sample=True,
+        use_cache=True
+    )
+    
+    # Decode response
+    response = processor.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+def main():
+    # Configuration
+    #checkpoint_path = "/path/to/your/checkpoint"
+    checkpoint_path = None
+    base_model_id = "HuggingFaceTB/SmolVLM-Instruct"  
+    video_path = "/path/to/video.mp4"
+    question = "Describe the video"
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    # Load model
+    logger.info("Loading model...")
+    model, processor = load_model(checkpoint_path, base_model_id, device)
+    
+    # Generate response
+    logger.info("Generating response...")
+    response = generate_response(model, processor, video_path, question)
+    
+    # Print results
+    print("Question:", question)
+    print("Response:", response)
+
+if __name__ == "__main__":
+    main()
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