12 citations · 17 across the 5 of their papers we have counts for
5 papers
Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
Hongtao Xu, Wenting Shen, Yuanxin Wei +6
Long-context supervised fine-tuning (Long-SFT) plays a vital role in enhancing the performance of large language models (LLMs) on long-context tasks. To smoothly adapt LLMs to long…
Wan: Open and Advanced Large-Scale Video Generative Models
Team Wan, Ang Wang, Baole Ai +58
This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transfo…
Efficient Long Context Fine-tuning with Chunk Flow
Xiulong Yuan, Hongtao Xu, Wenting Shen +10
Long context fine-tuning of large language models(LLMs) involves training on datasets that are predominantly composed of short sequences and a small proportion of longer sequences.…
BladeDISC++: Memory Optimizations Based On Symbolic Shape
Xiulong Yuan, Xu Yan, Wenting Shen +5
Recent deep learning workloads exhibit dynamic characteristics, leading to the rising adoption of dynamic shape compilers. These compilers can generate efficient kernels for dynami…
Structure Enhanced Graph Neural Networks for Link Prediction
Baole Ai, Zhou Qin, Wenting Shen +1
Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passin…