Fix typos and improve clarity in model creation summary in LiteRT documentation

Author: madeline-underwood

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

@@ -161,9 +161,9 @@ KleidiAI provides INT8 packing micro-kernels for both the activations and weight
 ## What you've accomplishee and what's next
 You have now created several LiteRT models with different quantization options, ready for benchmarking on your Arm-based Android device. You have:
 
-- Built a simple Keras model and converted it to LiteRT (`.tflite`) format.
-- Generated models with different quantization types: FP32, FP16, INT8 dynamic, and INT8 static.
-- Learned how each quantization method affects model size, performance, and compatibility with KleidiAI SME2 micro-kernels.
+- Built a simple Keras model and converted it to LiteRT (`.tflite`) format
+- Generated models with different quantization types: FP32, FP16, INT8 dynamic, and INT8 static
+- Learned how each quantization method affects model size, performance, and compatibility with KleidiAI SME2 micro-kernels
 
 Now that you have created and converted your models, you can benchmark them on your Android device to measure the performance gains from SME2 acceleration. Consider experimenting with additional layers such as Conv2D, BatchMatMul, or TransposeConv to further explore SME2 support for different operators. You can also apply more quantization and optimization techniques to enhance model efficiency. Finally, integrate your optimized models into your Android applications to leverage Arm SME2 acceleration in real-world use cases.