Update 4-build-model.md

Author: pareenaverma

content/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/4-build-model.md

@@ -65,10 +65,7 @@ Then, generate a model file on the `.pte` format using the Arm examples. The Ahe
 
 ```bash
 cd $ET_HOME
-python -m examples.arm.aot_arm_compiler --model_name=examples/arm/simple_nn.py \
---delegate --quantize --target=ethos-u85-256 \
---so_library=cmake-out-aot-lib/kernels/quantized/libquantized_ops_aot_lib.so \
---system_config=Ethos_U85_SYS_DRAM_Mid --memory_mode=Sram_Only
+python -m examples.arm.aot_arm_compiler --model_name=examples/arm/simple_nn.py --delegate --quantize --target=ethos-u85-256 --system_config=Ethos_U85_SYS_DRAM_Mid --memory_mode=Sram_Only
 ```
 
 From the Arm Examples directory, you can build an embedded Arm runner with the `.pte` included. This allows you to optimize the performance of your model, and ensures compatibility with the CPU kernels on the FVP. Finally, generate the executable `arm_executor_runner`.
@@ -122,4 +119,4 @@ I [executorch:arm_executor_runner.cpp:412] Model in 0x70000000 $
 I [executorch:arm_executor_runner.cpp:414] Model PTE file loaded. Size: 3360 bytes.
 ```
 
-You have now set up your environment for TinyML development on Arm, and tested a small PyTorch and ExecuTorch Neural Network. In the next Learning Path of this series, you will learn about optimizing neural networks to run on Arm.
+You have now set up your environment for TinyML development on Arm, and tested a small PyTorch and ExecuTorch Neural Network. In the next Learning Path of this series, you will learn about optimizing neural networks to run on Arm.