Converted content to more of a quick start guide for Hugging Face models on AMD devices using ROCm.

README.md

@@ -9,18 +9,89 @@ pinned: false
 # together we advance_AI
 
 AI is increasingly pervasive across the modern world. 
-It’s driving our smart technology in retail, cities, factories and healthcare, and transforming our digital homes. 
-AMD offers advanced AI acceleration from data center to edge, enabling high performance and high efficiency to make the world smarter.
+It’s driving our smart technology in retail, cities, factories and healthcare, 
+and transforming our digital homes. 
+AMD offers advanced AI acceleration from data center to edge, 
+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. 
+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.
+
+## 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?
+
+## 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.
+
+## 3. Install Machine Learning Frameworks
+Pip installation is an easy way to acquire all the required packages and is described in more detail below.
+
+>If you prefer to use a container strategy, check out the pre-built images at 
+[ROCm Docker Hub](https://hub.docker.com/u/rocm/) 
+and [AMD Infinity Hub](https://www.amd.com/en/technologies/infinity-hub) 
+after installing the required [dependancies](https://rocm.docs.amd.com/en/latest/deploy/docker.html).
+
+### PyTorch
+AMD ROCm is fully integrated into the mainline PyTorch ecosystem. Pip wheels are built and tested as part of the stable and nightly releases. 
+Go to [pytorch.org](https://pytorch.org) and use the 'Install PyTorch' widget. 
+Select 'Stable + Linux + Pip + Python + ROCm' to get the specific pip installation command.
+
+An example command line (note the versioning of the whl file): 
+  
+> `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2`
+
+### TensorFlow
+
+AMD ROCm is upstreamed into the TensorFlow github repository. 
+Pre-built wheels are hosted on [pipy.org](https://pypi.org/project/tensorflow-rocm/)
+
+The latest version can be installed with this command: 
+  
+> `pip install tensorflow-rocm`
+
+## 4. Use a Hugging Face Model
+Now that you have the base requirements installed, get the latest transformer models.
+
+> `pip install transformers`
+
+This allows you to easily import any of the base models into your python application.
+Here is an example using [GPT2](https://huggingface.co/gpt2) in PyTorch:
+
+```python
+from transformers import GPT2Tokenizer, GPT2Model
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2Model.from_pretrained('gpt2')
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+All of the 200+ standard transformer models are regularly tested with our supported hardware platforms.
+Note that this also implies that all derivatives of those core models should also function correctly. 
+Let us know if you run into issues at our [ROCm Community page](https://github.com/RadeonOpenCompute/ROCm/discussions)
+
+Here are a few of the more popular ones to get you started:
+- [BERT](https://huggingface.co/bert-base-uncased)
+- [BLOOM](https://huggingface.co/bigscience/bloom)
+- [LLaMA](https://huggingface.co/huggyllama/llama-7b)
+- [OPT](https://huggingface.co/facebook/opt-66b)
+- [T5](https://huggingface.co/t5-base)
+
+Click on the 'Use in Transformers' button to see the exact code to import a specific model into your Python application.
 
 # 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)
-- Is my [hardware supported](https://rocm.docs.amd.com/en/latest/release/gpu_os_support.html#gpu-support-table) with ROCm? 
-- ROCm [Documentation](https://rocm.docs.amd.com/en/latest/) for installation and usage
+- 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)
 
 
-# Run These HuggingFace Models with PyTorch on AMD GPUs
 
-- [BERT](https://huggingface.co/bert-base-uncased)
-- 
+
+