Updated links to Developer hub and ROCm documentation

README.md

@@ -34,11 +34,11 @@ General Linux and ML experience is a required pre-requisite.
 
 ## 1. Confirm you have a supported AMD hardware platform
 
-Is my [hardware supported](https://rocm.docs.amd.com/en/latest/release/gpu_os_support.html#gpu-support-table) with ROCm?
+Is my [hardware supported](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html) with ROCm on Linux?
 
 ## 2. Install ROCm driver, libraries and tools
 
-Follow the detailed [installation instructions](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html) for your Linux based platform.
+Follow the detailed [installation instructions](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/) for your Linux based platform.
 
 ## 3. Install Machine Learning Frameworks
 Pip installation is an easy way to acquire all the required packages and is described in more detail below.
@@ -117,7 +117,7 @@ across normal and distributed settings, with various supported optimizations and
 - Detailed Llama-2 results show casing the [Optimum benchmark on AMD Instinct MI250](https://huggingface.co/blog/huggingface-and-optimum-amd)
 - Check out our blog titled [Run a Chatgpt-like Chatbot on a Single GPU with ROCm](https://huggingface.co/blog/chatbot-amd-gpu)
 - Complete ROCm [Documentation](https://rocm.docs.amd.com/en/latest/) for installation and usage
-- Extended training content and connect with the development community at the [Developer Hub](https://www.amd.com/en/developer/rocm-hub.html)
+- Extended training content and connect with the development community at the [Developer Hub](https://www.amd.com/en/developer/resources/rocm-hub.html)