diff --git a/tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/README.md b/tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/README.md
index a313fa4ef..e7477b7ec 100644
--- a/tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/README.md
+++ b/tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/README.md
@@ -1,2 +1,38 @@
 This directory is modified based on default_8bit, which allows you to manually
 change the number of bits of weight and activation in QAT.
+
+Code example for quantization of a Keras float `model`:
+
+```
+# Imports.
+import tensorflow_model_optimization as tfmot
+
+from tensorflow_model_optimization.python.core.quantization.keras.quantize import quantize_annotate_model
+from tensorflow_model_optimization.python.core.quantization.keras.quantize import quantize_apply
+
+from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantize_scheme
+
+
+# TODO(user): define Keras float model.
+
+# Specify quantization scheme with 4-bit weights and 8-bit activations.
+qat_scheme_4w8a = default_n_bit_quantize_scheme.DefaultNBitQuantizeScheme(
+  num_bits_weight=4,
+  num_bits_activation=8,
+)
+
+# Prepare the model for quantized aware training.
+with tfmot.quantization.keras.quantize_scope():
+  quantized_aware_model = quantize_apply(
+    quantize_annotate_model(model),
+    qat_scheme_4w8a,
+  )
+
+# TODO(user): compile and train quantized_aware_model using standard Keras methods.
+```
+
+Recommended activation precision is 8-bit for TF Lite conversion.
+
+Before TF 2.11.0 the TF Lite converted weight value is stored one per byte in the weight tensor, so a 4-bit weight using default_n_bit scheme will be integer [-7, 7] occupying a byte.  With TF 2.11.0 and release candidate TF 2.12.0 weight packing for 4-bit weights is added for selected operators, so two 4-bit weights are packed per byte for the regular convolution operator in TF 2.11.0.
+
+To improve task quality it may be necessary to specify higher weight precision for the first and last layers of the model such as 8-bit.  This can be achieved using wrapper code per layer.  A code example is shown in [kws_streaming](https://github.com/google-research/google-research/commit/c87bac8133e00dc4fe646c182072676146312e0f) framework in Google Research repository.