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README.md

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 # Multi-Domain Expert Learning (M*DEL)**:
 ## How to increase knowledge without breaking the bank?
 
-🍩 Ontocord.AI 🍩 and the open source community.
+🍩 Ontocord.AI 🍩 and the open science community.
 
-M*DEL is a volunteer open source community for creating better mixtures of experts with volunteers from:
+M*DEL is a volunteer open science community for creating better mixtures of experts with volunteers from:
 Bedrock AI, TurkuNLP, ETH, Redmond.AI, Incite, MICS CentraleSupelec, Centro de Excelência em Inteligência Artificial, VietAI, Technion - Israel Institute of Technology, Nous Research, University of Western Australia, KoboldAI Community, LAION.AI, Mila,  Luleå University of Technology,  Juelich Supercomputing Center, Tokyo Tech, RIKEN, Together
 
 - [Try out our current proof of concept](https://huggingface.co/Multi-Domain-Expert-Layers/meow_1b/)
 
-Open sourcing AI models can lead to increased innovation, accessibility, transparency, and community building. However we need a mechanism to train more capable models in an efficient and modular way.
+OSS AI models can lead to increased innovation, accessibility, transparency, and community building. However we need a mechanism to train more capable models in an efficient and modular way.
 
-The proposed method that we call Multi-Domain Expert Learning (MDEL) for open source language models involves branching from a base model, training each branch independently on a specific domain for specific layers or other adapters, and merging the trained models at the end. Additionally, the specific layers or adapters are kept as experts, with a classifier used as a router to activate the experts during inference. This approach makes it possible to easily increase expertise of a model, to independently train more "adapters", and to reuse previously trained experts and models without retraining, resulting in a modular and efficient system. 
+The proposed method that we call Multi-Domain Expert Learning (MDEL) involves branching from a base model, training each branch independently on a specific domain for specific layers or other adapters, and merging the trained models at the end. Additionally, the specific layers or adapters are kept as experts, with a classifier used as a router to activate the experts during inference. This approach makes it possible to easily increase expertise of a model, to independently train more "adapters", and to reuse previously trained experts and models without retraining, resulting in a modular and efficient system. 
 
-In this effort, we seek international labs and open source aligned researchers and companies in various countries to each train a set of domain experts of their choosing, thereby enabling international participation and knowledge sharing. This will also result in lower costs for training and a lower environmental impact due to reuse and lower energy usage. Currently we have volunteers from four continents and are looking for more.
+In this effort, we seek international labs and open science aligned researchers and companies in various countries to each train a set of domain experts of their choosing, thereby enabling international participation and knowledge sharing. This will also result in lower costs for training and a lower environmental impact due to reuse and lower energy usage. Currently we have volunteers from four continents and are looking for more.
 
 We will be using a varient of the c-BTM (https://arxiv.org/pdf/2303.14177v1.pdf) method and will be focusing on models ranging from 7-70B parameters.