USTCdyf

USTCdyf's Stars

• geekan/MetaGPT

  🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

  Language:Python44.9k8956725.3k

• hiyouga/LLaMA-Factory

  Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)

  Language:Python33.7k2075.2k4.2k

• thuml/Time-Series-Library

  A Library for Advanced Deep Time Series Models.

  Language:Python6.9k735091.1k

• OpenTalker/video-retalking

  [SIGGRAPH Asia 2022] VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild

  Language:Python6.6k74245974

• MooreThreads/Moore-AnimateAnyone

  Character Animation (AnimateAnyone, Face Reenactment)

  Language:Python3.2k38152247

• KimMeen/Time-LLM

  [ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

  Language:Python1.4k16150247

• SalesforceAIResearch/uni2ts

  Unified Training of Universal Time Series Forecasting Transformers

  Language:Jupyter Notebook84096690

• chengtan9907/OpenSTL

  OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

  Language:Python7649149127

• thuml/TimesNet

  About Code release for "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" (ICLR 2023), https://openreview.net/pdf?id=ju_Uqw384Oq

  716102264

• DAMO-DI-ML/NeurIPS2023-One-Fits-All

  The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"

  Language:Python47055166

• XingYu-Zhong/LangChainStudy

  这个项目是一个Jupyter notebook的集合，专门用于学习和探索LangChain框架。

  Language:Jupyter Notebook3232274

• Hank0626/PDF

  An official implementation of "Periodicity Decoupling Framework for Long-term Series Forecasting" (ICLR 2024)

  Language:Python1063108

• Chengyui/NuwaTS

  NuwaTS: a Foundation Model Mending Every Incomplete Time Series

  Language:Python45513

• jinxixiang/magic_animate_unofficial

  An unoffical training code of Magic Animate

  Language:Python364165

• Lyccl/RGLT

  Language:Python10110

• CunWang520/RegularX

  This work studies the explainability of graph neural networks (GNNs), which is important for the credibility of GNNs in practical usage.

  Language:Jupyter Notebook30