Update README.md

README.md

@@ -14,8 +14,18 @@ _Model:_ ELM introduces a new type of _(de)-composable LLM model architecture_ a
 
 _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.
 
+- **Blog:** [Medium](https://medium.com/sujith-ravi/introducing-elm-efficient-customizable-privacy-preserving-llms-cea56e4f727d)
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+- **Github:** https://github.com/slicex-ai/elm-v0.1
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+- **Demo** (try it out): https://huggingface.co/spaces/slicexai/elm-demo-v1
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+- **HuggingFace** (access ELM Model cards, code & app from HF): https://huggingface.co/slicexai
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 ## ELM-v0.1 Model Release
-Models are located in the `models` folder. ELM models in this repository comes in two sizes (elm-1.0 and elm-0.75) and supports the following use-case.
+This repository contains code to run our ELM models. The current ELM model `elm-v0.1` (named _Rambutan_) was pre-trained (an intermediate checkpoint was used) and then instruction fine-tuned for downstream tasks.
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+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_summarization