The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their substantial demands on memory, processing power, and energy consumption. Spars...
Mistral AI , 开源AI模型 sparse mixture of experts model (SMoE) 号称Mixtral 8x7B 能力压Llama 2 70B,特别在inference速度上 Apache 2.0的授权 It is the strongest open-weight model with a permissive l...
Sparse Mixture of Experts (SMoE) has become the key to unlocking unparalleled scalability in deep learning. SMoE has the potential to exponentially increase parameter count while maintaining the efficiency of the model by only activating a small subset of these parameters for a given sample. However...
Triton-based implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and overcoming some of the limitations to improve inference, training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies ...
Our implementation is based onfastmoe repo,huggingface repoandSmoe-Dropout repo. Citation @inproceedings{ truong2023hyperrouter, title={HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts via HyperNetwork}, author={Truong Giang Do and Le Huy Khiem and TrungTin Nguyen an...
To address these challenges, we draw inspiration from the sparse mixture-of-agents (SMoE) and propose a sparse mixture-of-agents (SMoA) framework to improve the efficiency and diversity of multi-agent LLMs. Unlike completely connected structures, SMoA introduces novel Response Selection and Early...