diff --git a/ml/src/tft/quantized_attention.rs b/ml/src/tft/quantized_attention.rs
index 933c2b007..44b65f3a2 100644
--- a/ml/src/tft/quantized_attention.rs
+++ b/ml/src/tft/quantized_attention.rs
@@ -139,16 +139,24 @@ impl QuantizedTemporalAttention {
         // Dequantize all weights
         let q_weight = self
             .quantizer
-            .dequantize_tensor(self.q_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.q_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized Q weights not initialized".to_string())
+            })?)?;
         let k_weight = self
             .quantizer
-            .dequantize_tensor(self.k_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.k_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized K weights not initialized".to_string())
+            })?)?;
         let v_weight = self
             .quantizer
-            .dequantize_tensor(self.v_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.v_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized V weights not initialized".to_string())
+            })?)?;
         let o_weight = self
             .quantizer
-            .dequantize_tensor(self.o_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.o_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized O weights not initialized".to_string())
+            })?)?;
 
         // Store in cache
         self.attention_cache = Some(AttentionWeightCache {
@@ -229,7 +237,9 @@ impl QuantizedTemporalAttention {
                 // In production, cache should be built once after weight initialization
                 self.compute_projections_slow(x)?
             } else {
-                let cache = self.attention_cache.as_ref().unwrap();
+                let cache = self.attention_cache.as_ref().ok_or_else(|| {
+                    MLError::ModelError("attention weight cache not built".to_string())
+                })?;
 
                 // Reshape 3D input to 2D for batch matmul
                 let dims = x.dims();
@@ -312,7 +322,9 @@ impl QuantizedTemporalAttention {
 
         // Step 8: Output projection
         let output = if self.cache_enabled && self.attention_cache.is_some() {
-            let cache = self.attention_cache.as_ref().unwrap();
+            let cache = self.attention_cache.as_ref().ok_or_else(|| {
+                MLError::ModelError("attention weight cache not built".to_string())
+            })?;
             // Reshape attended to 2D for matmul, then reshape back
             let attended_2d = attended.reshape(&[batch_size * seq_len, self.hidden_dim])?;
             let output_2d = attended_2d.matmul(&cache.o_weight.t()?)?;
@@ -320,7 +332,9 @@ impl QuantizedTemporalAttention {
         } else {
             let o_weight = self
                 .quantizer
-                .dequantize_tensor(self.o_weights.as_ref().unwrap())?;
+                .dequantize_tensor(self.o_weights.as_ref().ok_or_else(|| {
+                    MLError::ModelError("quantized O weights not initialized".to_string())
+                })?)?;
             // Reshape attended to 2D for matmul, then reshape back
             let attended_2d = attended.reshape(&[batch_size * seq_len, self.hidden_dim])?;
             let output_2d = attended_2d.matmul(&o_weight.t()?)?;
@@ -334,13 +348,19 @@ impl QuantizedTemporalAttention {
     fn compute_projections_slow(&self, x: &Tensor) -> Result<(Tensor, Tensor, Tensor), MLError> {
         let q_weight = self
             .quantizer
-            .dequantize_tensor(self.q_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.q_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized Q weights not initialized".to_string())
+            })?)?;
         let k_weight = self
             .quantizer
-            .dequantize_tensor(self.k_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.k_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized K weights not initialized".to_string())
+            })?)?;
         let v_weight = self
             .quantizer
-            .dequantize_tensor(self.v_weights.as_ref().unwrap())?;
+            .dequantize_tensor(self.v_weights.as_ref().ok_or_else(|| {
+                MLError::ModelError("quantized V weights not initialized".to_string())
+            })?)?;
 
         // Reshape 3D input to 2D for batch matmul: [batch, seq_len, hidden_dim] -> [batch * seq_len, hidden_dim]
         // Candle doesn't support direct 3D x 2D matmul, so we flatten the batch and sequence dimensions
@@ -466,7 +486,9 @@ mod tests {
         attention.build_cache()?;
 
         // Manually verify attention weights sum to 1.0
-        let cache = attention.attention_cache.as_ref().unwrap();
+        let cache = attention.attention_cache.as_ref().ok_or_else(|| {
+            MLError::ModelError("attention weight cache not built".to_string())
+        })?;
         let num_heads = 8;
         let head_dim = hidden_dim / num_heads;