Machine Learning

FedML AI platform is democratizing large language models (LLMs) by enabling enterprises to train their own models on proprietary data. Today, we release FedLLM, an MLOps-supported training pipeline that allows for building domain-specific LLMs on proprietary data. The platform enables data collaboration, computation collaboration, and model collaboration, and supporting training on centralized and geo-distributed GPU clusters, as well as federated learning for data silos. FedLLM is compatible with popular LLM libraries such as HuggingFace and DeepSpeed, and is designed to improve efficiency and security/privacy. To get started, FedML users & developers only need to add 100 lines of source code. The complex steps of deployment and orchestration of training in enterprise environments are all handled by FedML MLOps platform.

We propose a novel algorithm that focuses on the less studied relationship between the unlabeled data. Our algorithm achieves state-of-the-art performance on standard benchmarks

We propose a novel algorithm for semi-supervised classification that achieves state-of-the-art performance on standard benchmarks and outperform previous works on transfer setting by a large margin.

This tutorial shows how to correctly implement EMA for PyTorch

We designed and evaluated a novel framework for robot mediation of a support group