Update description

Author: nikita-savelyevv

optimum/intel/openvino/configuration.py

@@ -865,9 +865,10 @@ def __init__(
             (1) weights of weighted layers to the precision given in the `weight_quantization_config`, and
             (2) weights and activations of other possible layers; precision is given in the `full_quantization_config`.
 
-        By default, all weighted layers are quantized in the first step. This leaves only non-weighted layers for the second step.
-        If some layers are instructed to be ignored in the first step with `weight_quantization_config.ignored_scope` parameter,
-        weights and activations of these layers are fully quantized to the precision given in the `full_quantization_config`.
+        By default, weights of all weighted layers are quantized in the first step. In the second step activations of
+        weighted and non-weighted layers are quantized. If some layers are instructed to be ignored in the first step
+        with `weight_quantization_config.ignored_scope` parameter, both weights and activations of these layers are
+        quantized to the precision given in the `full_quantization_config`.
 
         Args:
             weight_quantization_config (`OVWeightQuantizationConfig` or `dict`):

optimum/intel/openvino/quantization.py

@@ -1170,9 +1170,10 @@ def _mixed_quantization(
         (1) weights of weighted layers to the precision given in the `quantization_config.weight_quantization_config`, and
         (2) weights and activations of other possible layers; precision is given in the `quantization_config.full_quantization_config`.
 
-    By default, all weighted layers are quantized in the first step. This leaves only non-weighted layers for the second step.
-    If some weighted layers are instructed to be ignored in the first step with `weight_quantization_config.ignored_scope` parameter,
-    weights and activations of these layers are fully quantized to the precision given in the `quantization_config.full_quantization_config`.
+    By default, weights of all weighted layers are quantized in the first step. In the second step activations of
+    weighted and non-weighted layers are quantized. If some layers are instructed to be ignored in the first step
+    with `weight_quantization_config.ignored_scope` parameter, both weights and activations of these layers are
+    quantized to the precision given in the `full_quantization_config`.
 
     Args:
         model (`openvino.runtime.Model`):