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README.md

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+This repository contains configurations to slice Gemma 3n E4B, which is enabled thanks to it being a MatFormer. 
+The E4B model can be sliced into small models, trading off quality and latency/compute requirements.
+We recommend exploring the [MatFormer Lab](TODO: add link) to getting started with slicing Gemma 3n E4B yourself.
+
+For each configuration, we calculate the MMLU accuracy.
+Although these are not the only configurations possible, they are optimal configurations
+identified by calculating the accuracy of the pre-trained model 
+
+To learn more about MatFormers, please review the  and generate your own submodels
+with the [MatFormer Lab](TODO: add link).
+
+![alt text](https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/Artboard_1.original.png  "This chart show’s MMLU performance vs model size of Gemma 3n Mix-n-Match (pretrained) capability.")
+This chart show’s MMLU performance vs model size of Gemma 3n Mix-n-Match (pretrained) capability.
+
+Some additional resources:
+
+* [Gemma 3n launch blog](https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide)
+* [MatFormer paper](https://huggingface.co/papers/2310.07707)