diff --git a/src/aiunn/finetune.py b/src/aiunn/finetune.py
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+++ b/src/aiunn/finetune.py
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+import torch
+import pandas as pd
+from PIL import Image
+import io
+from torch import nn
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as transforms
+from aiia.model import AIIABase
+from sklearn.model_selection import train_test_split
+
+
+# Step 1: Define Custom Dataset Class
+class ImageDataset(Dataset):
+    def __init__(self, dataframe, transform=None):
+        self.dataframe = dataframe
+        self.transform = transform
+        
+    def __len__(self):
+        return len(self.dataframe)
+    
+    def __getitem__(self, idx):
+        row = self.dataframe.iloc[idx]
+        
+        # Decode image_512 from bytes
+        img_bytes = row['image_512']
+        img_stream = io.BytesIO(img_bytes)
+        low_res_image = Image.open(img_stream).convert('RGB')
+        
+        # Decode image_1024 from bytes
+        high_res_bytes = row['image_1024']
+        high_stream = io.BytesIO(high_res_bytes)
+        high_res_image = Image.open(high_stream).convert('RGB')
+        
+        # Apply transformations if specified
+        if self.transform:
+            low_res_image = self.transform(low_res_image)
+            high_res_image = self.transform(high_res_image)
+            
+        return {'low_res': low_res_image, 'high_res': high_res_image}
+
+
+
+# Step 2: Load and Preprocess Data
+# Read the dataset (assuming it's a DataFrame with columns 'image_512' and 'image_1024')
+df1 = pd.read_parquet('/root/training_data/vision-dataset/image_upscaler.parquet')
+df2 = pd.read_parquet('/root/training_data/vision-dataset/image_vec_upscaler.parquet')
+
+# Combine the two datasets into one DataFrame
+df = pd.concat([df1, df2], ignore_index=True)
+
+# Split into training and validation sets
+train_df, val_df = train_test_split(df, test_size=0.2, random_state=42)
+
+# Define preprocessing transforms
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+train_dataset = ImageDataset(train_df, transform=transform)
+val_dataset = ImageDataset(val_df, transform=transform)
+
+# Create DataLoaders
+batch_size = 2
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
+val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
+
+# Step 3: Load Pre-trained Model and Modify for Upscaling
+model = AIIABase.load("AIIA-Base-512")
+
+# Freeze original CNN layers to prevent catastrophic forgetting
+for param in model.cnn.parameters():
+    param.requires_grad = False
+
+# Add upsample module
+hidden_size = model.config.hidden_size  # Assuming this is defined in your model's config
+model.upsample = torch.nn.Sequential(
+    nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+    nn.Conv2d(hidden_size, 3, kernel_size=3, padding=1)
+)
+
+# Step 4: Define Loss Function and Optimizer
+criterion = torch.nn.MSELoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)  # Adjust learning rate as needed
+
+# Alternatively, if you want to train only the new layers:
+params_to_update = []
+for name, param in model.named_parameters():
+    if 'upsample' in name:
+        params_to_update.append(param)
+optimizer = torch.optim.Adam(params_to_update, lr=0.001)
+
+# Step 5: Training Loop
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+best_val_loss = float('inf')
+num_epochs = 10  # Adjust as needed
+
+for epoch in range(num_epochs):
+    model.train()
+    running_loss = 0.0
+    
+    for batch in train_loader:
+        low_res = batch['low_res'].to(device)
+        high_res = batch['high_res'].to(device)
+        
+        # Forward pass
+        features = model.cnn(low_res)
+        outputs = model.upsample(features)
+        
+        loss = criterion(outputs, high_res)
+        
+        # Backward pass and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        running_loss += loss.item()
+    
+    epoch_loss = running_loss / len(train_loader)
+    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}')
+    
+    # Validation Step
+    model.eval()
+    val_loss = 0.0
+    
+    with torch.no_grad():
+        for batch in val_loader:
+            low_res = batch['low_res'].to(device)
+            high_res = batch['high_res'].to(device)
+            
+            features = model.cnn(low_res)
+            outputs = model.upsample(features)
+            
+            loss = criterion(outputs, high_res)
+            val_loss += loss.item()
+
+    print(f"Validation Loss: {val_loss:.4f}")
+    
+    if val_loss < best_val_loss:
+        best_val_loss = val_loss
+        model.save("AIIA-base-512-upscaler")
+        print("Best model saved!")
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