Update 3-buid-model.md

Author: pareenaverma

content/learning-paths/mobile-graphics-and-gaming/litert-sme/3-buid-model.md

@@ -51,7 +51,14 @@ TransposeConv:
 ### Create LiteRT models using Keras
 To demonstrate SME2 acceleration on Android, you will construct simple single-layer models (e.g., Fully Connected) using Keras and convert them into LiteRT (.tflite) format.
 This allows you to benchmark isolated operators and directly observe SME2 improvements.
-Use the following script as an example:
+
+Install the tensorflow package dependency for your script:
+
+```bash
+sudo pip3 install tensorflow
+```
+
+Save the following script as an example in a file named `model.py`:
 
 ``` python
 import tensorflow as tf
@@ -79,6 +86,12 @@ fc_fp32 = converter.convert()
 save_litert_model(fc_fp32, "fc_fp32.tflite")
 ```
 
+Now run the script:
+
+```bash
+python3 model.py
+```
+
 The model `fc_fp32.tflite` is created in FP32 format. As mentioned in the previous section, this operator can invoke the KleidiAI SME2 micro-kernel for acceleration.
 
 You can also optimize this Keras model using post-training quantization to create a LiteRT model that suits your requirements.