The University of Hong Kong

HybridFlow is the result of a joint research project between ByteDance's Beanbag Big Model team and the University of Hong Kong. Officially, HybridFlow (open source project name: veRL) is a flexible and efficient RL training framework for big models, compatible with a variety of training and inference frameworks, and supporting flexible model deployment and Multiple RL algorithm implementation. The framework uses a hybrid programming model that combines the flexibility of Single-Controller and the efficiency of Multi-Controller to better realize and execute multiple...

Recently, the University of Hong Kong has released OpenGraph, a breakthrough achievement that successfully overcomes three major challenges in the field of graph-based modeling. The model achieves zero-sample learning through clever techniques that can be adapted to a variety of downstream tasks.The construction of OpenGraph is divided into three main parts: a unified graph Tokenizer, an extensible graph Transformer, and knowledge distillation for large language models. OpenGraph solves the problem of variation in node set and feature space between different datasets by creating a unified graph Tokenizer. A topology-aware mapping approach is used ...