diff --git a/src/aiia/pretrain/pretrainer.py b/src/aiia/pretrain/pretrainer.py
index 42ba4b8..94d412d 100644
--- a/src/aiia/pretrain/pretrainer.py
+++ b/src/aiia/pretrain/pretrainer.py
@@ -1,6 +1,8 @@
 import torch
 from torch import nn
 import csv
+import datetime
+import time
 import pandas as pd
 from tqdm import tqdm
 from ..model.Model import AIIA
@@ -112,18 +114,21 @@ class Pretrainer:
             
         return batch_loss
 
-    def train(self, dataset_paths,output_path:str="AIIA", column="image_bytes",  num_epochs=3, batch_size=2, sample_size=10000):
-        """
-        Train the model using multiple specified datasets.
-        
-        Args:
-            dataset_paths (list): List of paths to parquet datasets
-            num_epochs (int): Number of training epochs
-            batch_size (int): Batch size for training
-            sample_size (int): Number of samples to use from each dataset
-        """
+    def train(self, dataset_paths, output_path="AIIA", column="image_bytes",
+            num_epochs=3, batch_size=2, sample_size=10000, checkpoint_dir=None):
+        """Train the model using multiple specified datasets."""
         if not dataset_paths:
             raise ValueError("No dataset paths provided")
+        
+        # Checkpoint tracking variables
+        last_checkpoint_time = time.time()
+        checkpoint_interval = 2 * 60 * 60  # 2 hours in seconds
+        last_22_date = None
+        recent_checkpoints = []
+        
+        # Create checkpoint directory if specified
+        if checkpoint_dir is not None:
+            os.makedirs(checkpoint_dir, exist_ok=True)
 
         # Read and merge all datasets
         dataframes = []
@@ -166,6 +171,59 @@ class Pretrainer:
                 if batch_data is None:
                     continue
                 
+                # Check if we need to save a checkpoint
+                current_time = time.time()
+                current_dt = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=2)))  # German time
+                today = current_dt.date()
+                
+                # Regular 2-hour checkpoint
+                if checkpoint_dir and (current_time - last_checkpoint_time) >= checkpoint_interval:
+                    checkpoint_path = os.path.join(
+                        checkpoint_dir, 
+                        f"checkpoint_epoch{epoch+1}_batch{batch_count}.pt"
+                    )
+                    torch.save({
+                        'epoch': epoch + 1,
+                        'batch': batch_count,
+                        'model_state_dict': self.model.state_dict(),
+                        'projection_head_state_dict': self.projection_head.state_dict(),
+                        'optimizer_state_dict': self.optimizer.state_dict(),
+                        'train_losses': self.train_losses,
+                        'val_losses': self.val_losses,
+                    }, checkpoint_path)
+                    
+                    # Track and maintain only 3 recent checkpoints
+                    recent_checkpoints.append(checkpoint_path)
+                    if len(recent_checkpoints) > 3:
+                        oldest = recent_checkpoints.pop(0)
+                        if os.path.exists(oldest):
+                            os.remove(oldest)
+                            
+                    last_checkpoint_time = current_time
+                    print(f"Checkpoint saved at {checkpoint_path}")
+                    
+                # Special 22:00 checkpoint
+                is_22_oclock = current_dt.hour == 22 and current_dt.minute == 0 and current_dt.second < 10
+                
+                if checkpoint_dir and is_22_oclock and last_22_date != today:
+                    checkpoint_path = os.path.join(
+                        checkpoint_dir, 
+                        f"checkpoint_22h_{today.strftime('%Y%m%d')}.pt"
+                    )
+                    torch.save({
+                        'epoch': epoch + 1,
+                        'batch': batch_count,
+                        'model_state_dict': self.model.state_dict(),
+                        'projection_head_state_dict': self.projection_head.state_dict(),
+                        'optimizer_state_dict': self.optimizer.state_dict(),
+                        'train_losses': self.train_losses,
+                        'val_losses': self.val_losses,
+                    }, checkpoint_path)
+                    
+                    last_22_date = today
+                    print(f"22:00 Checkpoint saved at {checkpoint_path}")
+                
+                # Process the batch
                 self.optimizer.zero_grad()
                 batch_loss = self._process_batch(batch_data, criterion_denoise, criterion_rotate)
                 
@@ -192,6 +250,7 @@ class Pretrainer:
         losses_path = os.path.join(os.path.dirname(output_path), 'losses.csv')
         self.save_losses(losses_path)
 
+
     def _validate(self, val_loader, criterion_denoise, criterion_rotate):
         """Perform validation and return average validation loss."""
         val_loss = 0.0