diff --git a/src/layers/normalization.rs b/src/layers/normalization.rs
index 12e026f..48ff1dc 100644
--- a/src/layers/normalization.rs
+++ b/src/layers/normalization.rs
@@ -19,18 +19,18 @@ pub fn layer_norm(
     // Step 1: Calculate mean and variance across the features (axis=1)
     let mean = x.mean_axis(Axis(1)).unwrap();
     let variance = x.var_axis(Axis(1), 0.0);
-    println!("Mean: {:?}", mean);
-    println!("Variance: {:?}", variance);
+    //println!("Mean: {:?}", mean);
+    // println!("Variance: {:?}", variance);
 
     let expanded_mean = mean.insert_axis(Axis(1)); // Expands [6] to [6, 1]
     let expanded_variance = variance.insert_axis(Axis(1)); // Expands [6] to [6, 1]
-    println!("EXPMean: {:?}", expanded_mean);
-    println!("EXPVariance: {:?}", expanded_variance);
+                                                           // println!("EXPMean: {:?}", expanded_mean);
+                                                           //println!("EXPVariance: {:?}", expanded_variance);
 
     // Add epsilon to expanded variance
     let normalized = (x - &expanded_mean) / (expanded_variance + epsilon).mapv(f32::sqrt);
 
-    println!("Normalized {}", normalized);
+    // println!("Normalized {}", normalized);
     // Step 2: Normalize the input
     //let normalized = (x - &reshaped_mean) / (reshaped_variance + epsilon).mapv(f32::sqrt);
 
diff --git a/src/model/encoder.rs b/src/model/encoder.rs
index 9789089..c8ed88f 100644
--- a/src/model/encoder.rs
+++ b/src/model/encoder.rs
@@ -41,7 +41,7 @@ pub fn encoding(
         dummy_learned_matrices.clone(), // W_O
     );
 
-    println!("Attention1 :{}", attention_output);
+    //println!("Attention1 :{}", attention_output);
     // Add & Normalize (Residual Connection + Layer Norm)
     let attention_residual = attention_output.add(&input); // Residual connection
     let reshaped_attention = attention_residual
@@ -60,7 +60,7 @@ pub fn encoding(
     // Feed-Forward Network
     let feed_forward_output = feed_forward_layer.forward(attention_norm.clone());
 
-    println!("feed_forward_output :{:?}", feed_forward_output);
+    //println!("feed_forward_output :{:?}", feed_forward_output);
     //  Add & Normalize (Residual Connection + Layer Norm)
     let feed_forward_residual = feed_forward_output.add(&attention_norm); // Residual connection
     let reshaped_ff_attention = feed_forward_residual