Update README.md

README.md

@@ -1,17 +1,37 @@
+---
+license: apache-2.0
+---
 # SliceX AI™ ELM (Efficient Language Models)
-This repository contains code to run our ELM models.
+**ELM** (which stands for **E**fficient **L**anguage **M**odels) is the first version in the series of cutting-edge language models from [SliceX AI](https://slicex.ai) that is designed to achieve the best in class performance in terms of _quality_, _throughput_ & _memory_.
 
-Models are located in the "models" folder. ELM models in this repository comes in three sizes (elm-1.0, elm-0.75 and elm-0.25) and supports the following use-case.
+<div align="center">
+  <img src="elm-rambutan.png" width="256"/>
+</div>
+
+ELM is designed to be a modular and customizable family of neural networks that are highly efficient and performant. Today we are sharing the first version in this series: **ELM-v0.1** models. 
+
+_Model:_ ELM introduces a new type of _(de)-composable LLM model architecture_ along with the algorithmic optimizations required to learn (training) and run (inference) these models. At a high level, we train a single ELM model in a self-supervised manner (during pre-training phase) but once trained the ELM model can be sliced in many ways to fit different user/task needs. The optimizations can be applied to the model either during the pre-training and/or fine-tuning stage. 
+
+_Fast Inference with Customization:_ Once trained, the ELM model architecture permits flexible inference strategies at runtime depending on the deployment needs. For instance, the ELM model can  be _decomposed_ into smaller slices, i.e., smaller (or larger) models can be extracted from the original model to create multiple inference endpoints. Alternatively, the original (single) ELM model can be loaded _as is_ for inference and different slices within the model can be queried directly to power faster inference. This provides an additional level of flexibility for users to make compute/memory tradeoffs depending on their application and runtime needs.
+
+## ELM-v0.1 Model Release
+Models are located in the `models` folder. ELM models in this repository comes in three sizes (elm-1.0, elm-0.75 and elm-0.25) and supports the following use-case.
 - news_classification
 
-Try out the ELM models in HF spaces at [slicexai/elm-demo-v1](https://huggingface.co/spaces/slicexai/elm-demo-v1)
 
-## Download ELM repo
+## Setup ELM
+### Download ELM repo
 ```bash
+git clone git@hf.co:slicexai/elm-v0.1_news_classification
 sudo apt-get intall git-lfs 
 git lfs install
-git clone git@hf.co:slicexai/elm-v0.1_news_classification
 ```
+### Installation
+```bash
+cd elm-v0.1_news_news_classification
+pip install -r requirements.txt
+```
+
 (Optional) Installing git-lfs without sudo,
 ```bash
 wget https://github.com/git-lfs/git-lfs/releases/download/v3.2.0/git-lfs-linux-amd64-v3.2.0.tar.gz
@@ -21,12 +41,6 @@ git lfs install
 ```
 
 
-## Installation
-```bash
-cd elm-v0.1_news_classification
-pip install -r requirements.txt
-```
-
 ## How to use - Run ELM on a sample task
 ```bash
 python run.py <elm-model-directory>