Lists (1)
Stars
Low overhead tracing library and trace visualizer for pipelined CUDA kernels
微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。
Kronos: A Foundation Model for the Language of Financial Markets
Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.
[NeurIPS 2025] Radial Attention: O(nlogn) Sparse Attention with Energy Decay for Long Video Generation
[ICML2025, NeurIPS2025 Spotlight] Sparse VideoGen 1 & 2: Accelerating Video Diffusion Transformers with Sparse Attention
Enjoy the magic of Diffusion models!
Utility scripts for PyTorch (e.g. Make Perfetto show some disappearing kernels, Memory profiler that understands more low-level allocations such as NCCL, ...)
This project involves using a combination of statistics along with financial thoery to demonstrate a popular trading strategy used in equity markets: Pairs Trading.
High performance self-hosted photo and video management solution.
MAGI-1: Autoregressive Video Generation at Scale
A TTS model capable of generating ultra-realistic dialogue in one pass.
OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched
[ICCV 2023] Q-Diffusion: Quantizing Diffusion Models.
A curated list of recent diffusion models for video generation, editing, and various other applications.
[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.
Performant and effortless finance plotting for Python
FinRL®: Financial Reinforcement Learning. 🔥
Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
FinHack®，一个易于拓展的量化金融框架，它在当前版本中集成了数据采集、因子计算、因子挖掘、因子分析、机器学习、策略编写、量化回测、实盘接入等全流程的量化投研工作。
Sky-T1: Train your own O1 preview model within $450
Agent Laboratory is an end-to-end autonomous research workflow meant to assist you as the human researcher toward implementing your research ideas
My learning notes for ML SYS.