[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

Code for our paper "VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters".

Free English to Chinese Dictionary Database

Official code repository for paper "VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Visual Backbones"

Emacs libvterm integration

Time Vision Transformer

Experimental code for wavelet GPT and Fourier series expression

WavSpA: Wavelet Space Attention for Enhancing Transformer's Long Sequence Learning

Emacs libvterm integration

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

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers."

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

The official implementation of "TS-Agent: Reinforcement Learning Empowered LLM Agents for Financial Time Series Forecasting"

[EMNLP-2025] R1-Zero on ANY TASK

Emacs major mode for working with mermaid graphs https://mermaidjs.github.io/

本科华五，曾赴美qs50读博，某兄弟院校副教授，校园门卫亭女性主理人，为防止炸号的备份平台，是本人。

A benchmark for LLMs on complicated tasks in the terminal

CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning [EMNLP 2025-main]

Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures

[ICML 2026] BizFinBench.v2: A Unified Offline–Online Bilingual Benchmark for Expert-Level Financial Capability Evaluation of LLMs

Collection of evals for Inspect AI

WinoGrande: An Adversarial Winograd Schema Challenge at Scale

HellaSwag: Can a Machine _Really_ Finish Your Sentence?

Measuring Massive Multitask Language Understanding | ICLR 2021

A quick guide (especially) for trending instruction finetuning datasets

LongMIT: Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)