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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

Large Language Models (LLMs) are changing how people interact with information, increasing global productivity and abruptly shifting markets. Companies have been leveraging this technology to integrate it into their products or business processes using third-party APIs, but the proof-of-concept era has ended. Now, it’s time to differentiate LLMs-powered products and provide added value to your customers.Open source models are the best way to achieve transparency and secure setups. When fed with proprietary data, there’s a clear differential competitive advantage. Fine-tuned LLMs are more adapted to the context they are intended to deal with by using high-quality data. With cloud platforms…

arXiv:2407.03622v1 Announce Type: new Abstract: Training large models requires a large amount of data, as well as abundant computation resources. While collaborative learning (e.g., federated learning) provides a promising paradigm to harness collective data from many participants, training large models remains a major challenge for participants with limited resources like mobile devices. We introduce MSfusion, an effective and efficient collaborative learning framework, tailored for training larger models on resourceconstraint machines through model splitting. Specifically, a double shifting model splitting scheme is designed such that in each training round, each participant is assigned a subset of model parameters to train over…