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Multi-Domain Expert Layers (MDEL) Training:
How to increase knowledge without breaking the bank?
Volunteers from: Ontocord.AI, 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, LAION.AI
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.
The proposed method that we call Multi-Domain Expert Layers (MDEL) training for open source language models involves branching from a base model, training each branch independently on a specific domain for specific layers, and merging the trained models at the end. Additionally, the specific layers 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.
We will be using a varient of the c-BTM (https://arxiv.org/pdf/2303.14177v1.pdf) method and will be focusing on models around 20B parameters.
If you are interested in contributing to this project, please reach out to us and learn more about how you can get involved at [email protected].
Let's work together to create open-source models that benefit everyone! 🤝 #AI #MDEL #Supercomputers #Summit #OpenSource #Innovation #VolunteersNeeded #OpenScience #DemocratizeAI