Update README.md

README.md

@@ -18,11 +18,10 @@ enabling high performance and high efficiency to make the world smarter.
 
 # Getting Started with Hugging Face Transformers
 
-Details on getting started 
-with Hugging Face models are available on the [Optimum page](https://huggingface.co/docs/optimum/main/en/amd/index) 
 
-The following section describes how to use the most common transformers on Hugging Face 
-for inference workloads on select AMD Instinct™ accelerators and AMD Radeon™ GPUs using the AMD ROCm software ecosystem. 
+
+Looking for how to use the most common transformers on Hugging Face 
+for inference workloads on select AMD Instinct™ accelerators and AMD Radeon™ GPUs using the AMD ROCm software ecosystem? 
 This base knowledge can be leveraged to start fine-tuning from a base model or even start developing your own model.
 General Linux and ML experience is a required pre-requisite.
 
@@ -94,6 +93,9 @@ Click on the 'Use in Transformers' button to see the exact code to import a spec
 For a deeper dive into using Hugging Face libraries on AMD GPUs, check out the [Optimum](https://huggingface.co/docs/optimum/main/en/amd/amdgpu/overview) page 
 describing details on Flash Attention 2, GPTQ Quantization and ONNX Runtime integration.  
 
+Details on getting started 
+with Hugging Face models are available on the [Optimum page](https://huggingface.co/docs/optimum/main/en/amd/index) 
+
 # Serving a model with TGI
 
 Text Generation Inference (a.k.a “TGI”) provides an end-to-end solution to deploy large language models for inference at scale.