Update retriever.py

retriever.py

@@ -22,12 +22,11 @@ def pruning():
     Optimizes models by removing unnecessary components, such as certain weights in a neural network. 
     This function demonstrates how to apply pruning.
     """
-    model = apply_pruning(model, amount=0.3)
-    code = "model = apply_pruning(model, amount=0.3)"  
+    code = "model = apply_pruning(model, amount=0.3)"" 
     return (
             f"To apply pruning to a model, use the following code snippet: {code}. "
             f"You should adapt it to your actual implementation. In particular, the 'amount' parameter "
-            f"can be increased or decreased depending on the initial number of weights and the complexity of your use case (minimu value: 0, maximum value: 1)."
+            f"can be increased or decreased depending on the initial number of weights and the complexity of your use case (minimu value: 0, maximum value: 1). You should give these information to the manager, you can not prune the model yourself."
         )
 
 def quantization():
@@ -36,5 +35,5 @@ def quantization():
     """
     code = "model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)"
     return (
-            f"To apply quantization to a model, use the following code snippet: {code}."
+            f"To apply quantization to a model, use the following code snippet: {code}. You should give these information to the manager, you can not quantize the model yourself."
         )