diff --git a/model_training_nnn_tpu/rnn_model_tf.py b/model_training_nnn_tpu/rnn_model_tf.py
index 4da2f28..273f4d9 100644
--- a/model_training_nnn_tpu/rnn_model_tf.py
+++ b/model_training_nnn_tpu/rnn_model_tf.py
@@ -759,20 +759,74 @@ class CTCLoss(keras.losses.Loss):
 # TPU Strategy Helper Functions
 def create_tpu_strategy():
     """Create TPU strategy for distributed training on TPU v5e-8"""
+    import os
+
+    print("🔍 Detecting TPU environment...")
+
+    # Check for various TPU environment variables
+    tpu_address = None
+    tpu_name = None
+
+    # Check common TPU environment variables
+    if 'COLAB_TPU_ADDR' in os.environ:
+        tpu_address = os.environ['COLAB_TPU_ADDR']
+        print(f"📍 Found Colab TPU address: {tpu_address}")
+    elif 'TPU_NAME' in os.environ:
+        tpu_name = os.environ['TPU_NAME']
+        print(f"📍 Found TPU name: {tpu_name}")
+    elif 'TPU_WORKER_ID' in os.environ:
+        # Kaggle TPU environment
+        worker_id = os.environ.get('TPU_WORKER_ID', '0')
+        tpu_address = f'grpc://10.0.0.2:8470'  # Default Kaggle TPU address
+        print(f"📍 Kaggle TPU detected, worker ID: {worker_id}, address: {tpu_address}")
+
+    # Print all TPU-related environment variables for debugging
+    print("🔧 TPU environment variables:")
+    tpu_vars = {k: v for k, v in os.environ.items() if 'TPU' in k or 'COLAB' in k}
+    for key, value in tpu_vars.items():
+        print(f"   {key}={value}")
+
     try:
+        # Try different TPU resolver configurations
+        resolver = None
+
+        if tpu_address:
+            print(f"🚀 Attempting TPU connection with address: {tpu_address}")
+            resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=tpu_address)
+        elif tpu_name:
+            print(f"🚀 Attempting TPU connection with name: {tpu_name}")
+            resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=tpu_name)
+        else:
+            # Try auto-detection
+            print("🚀 Attempting TPU auto-detection...")
+            resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
+
         # Initialize TPU
-        resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
+        print("⚡ Connecting to TPU cluster...")
         tf.config.experimental_connect_to_cluster(resolver)
+
+        print("🔧 Initializing TPU system...")
         tf.tpu.experimental.initialize_tpu_system(resolver)
 
         # Create TPU strategy
+        print("🎯 Creating TPU strategy...")
         strategy = tf.distribute.TPUStrategy(resolver)
-        print(f"TPU initialized successfully. Number of replicas: {strategy.num_replicas_in_sync}")
+
+        print(f"✅ TPU initialized successfully!")
+        print(f"🎉 Number of TPU cores: {strategy.num_replicas_in_sync}")
+        print(f"🏃 TPU cluster: {resolver.cluster_spec()}")
         return strategy
 
-    except ValueError as e:
-        print(f"Failed to initialize TPU: {e}")
-        print("Falling back to default strategy")
+    except Exception as e:
+        print(f"❌ Failed to initialize TPU: {e}")
+        print(f"🔍 Error type: {type(e).__name__}")
+
+        # Enhanced error reporting
+        if "Please provide a TPU Name" in str(e):
+            print("💡 Hint: TPU name/address not found in environment variables")
+            print("   Common variables: COLAB_TPU_ADDR, TPU_NAME, TPU_WORKER_ID")
+
+        print("🔄 Falling back to default strategy (CPU/GPU)")
         return tf.distribute.get_strategy()