Updated with Ryzen AI, TGI and Benchmarking sections

README.md

@@ -16,8 +16,16 @@ enabling high performance and high efficiency to make the world smarter.
 
 # Getting Started with Hugging Face Transformers
 
-This section describes how to use the most common transformers on Hugging Face 
-for inference workloads on AMD accelerators using the AMD ROCm software ecosystem. 
+AMD’s Ryzen™ AI family of laptop processors provide users with an integrated Neural Processing Unit (NPU) 
+which offloads the host CPU and GPU from AI processing tasks. Ryzen™ AI software consists of the Vitis ™ AI 
+execution provider (EP) for ONNX Runtime combined with quantization tools and a pre-optimized model zoo. 
+All of this is made possible based on Ryzen™ AI technology built on AMD XDNA™ architecture, 
+purpose-built to run AI workloads efficiently and locally, 
+offering a host of benefits for the developer innovating the next groundbreaking AI app. Details on getting started 
+with Hugging Face models are available on the [Optimum page](https://moon-ci-docs.huggingface.co/docs/optimum-amd/pr_29/en/ryzenai/overview) 
+
+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. 
 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.
 
@@ -85,6 +93,18 @@ Here are a few of the more popular ones to get you started:
 
 Click on the 'Use in Transformers' button to see the exact code to import a specific model into your Python application.
 
+# 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. 
+TGI is already usable in production on AMD Instinct™ GPUs through the docker image `ghcr.io/huggingface/text-generation-inference:1.2-rocm`. 
+Make sure to refer to the [documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware) 
+concerning the support and any limitations. 
+
+# Benchmarking
+
+The [Optimum-Benchmark](https://github.com/huggingface/optimum-benchmark) is available as a utility to easily benchmark the performance of transformers on AMD GPUs, 
+across normal and distributed settings, with various supported optimizations and quantization schemes.     
+
 # Useful Links and Blogs
 
 - Check out our blog titled [Run a Chatgpt-like Chatbot on a Single GPU with ROCm](https://huggingface.co/blog/chatbot-amd-gpu)