I would like to introduce our paper about Pipeline Parallelism that has been accepted by SC23. We proposed a new pipeline parallelism scheme that reduces the bubble ratio and increases the model training throughput. The full paper is available at arxiv.org/abs/2308.15762.

Really enjoyed chipping in on this project. It's been awesome!😃

We are excited that Shuaiwen Leon Song is joining the stellar Together AI Research team as VP Frontier Technologies. Leon has driven LLM ecosystem innovations such as Deepspeed and Project Brainwave. Leon will help our mission to build the fastest cloud for generative AI.

Training-free Video Enhancement: Achieved 🎉 Nice work with Xuanlei Zhao Wenqi Shaw Victor.Kai Wang @VitaGroupUT Yang You et al. Non-trivial enhancement, training-free, and plug-and-play 🥳 Blog: oahzxl.github.io/Enhance_A_Vide… (🧵1/6)

How can we better animate images solely following text descriptions? We present Motion Focal Loss (MotiF) (arxiv.org/abs/2412.16153) to better align motions with text descriptions in text-image-to-video (TI2V) task and release TI2V-Bench, a comprehensive TI2V benchmark. (1/n)

Generating ~200 million parameters in just minutes! 🥳 Excited to share our work with Doven Tang , ZHAO WANGBO , and Yang You: 'Recurrent Diffusion for Large-Scale Parameter Generation' (RPG for short). Example: Obtain customized models using prompts (see below). (🧵1/8)

RAS yields over 2x the acceleration with almost no image quality loss! Nice work and congrats!

Customizing Your LLMs in seconds using prompts🥳! Excited to share our latest work with HPC-AI Lab, VITA Group, Konstantin Schürholt, Yang You, Michael Bronstein, Damian Borth : Drag-and-Drop LLMs(DnD). 2 features: tuning-free, comparable or even better than full-shot tuning.(🧵1/8)