Stars
分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.
A high-throughput and memory-efficient inference and serving engine for LLMs
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
🍰 Desktop utility to download images/videos/music/text from various websites, and more.
Scapy: the Python-based interactive packet manipulation program & library.
💮 amazing QRCode generator in Python (supporting animated gif) - Python amazing 二维码生成器（支持 gif 动态图片二维码）
Emulator for rapid prototyping of Software Defined Networks
📚A curated list of Awesome LLM/VLM Inference Papers with Codes: Flash-Attention, Paged-Attention, WINT8/4, Parallelism, etc.🎉
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o…
A unified framework for privacy-preserving data analysis and machine learning
An easy-to-use federated learning platform
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
Federated Learning Benchmark - Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)
FedScale is a scalable and extensible open-source federated learning (FL) platform.
Repository to host and maintain SCALE-Sim code
A novel approach for synthesizing tabular data using pretrained large language models
Tools and service for differentially private processing of tabular and relational data
ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations [COLM 2025]
[NeurIPS'24 Oral] HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
A codebase that makes differentially private training of transformers easy.
Differentially-private transformers using HuggingFace and Opacus
GRACE - GRAdient ComprEssion for distributed deep learning
Fine-tuning Vision Transformers on various classification datasets
Python Module for Huffman Encoding and Decoding
A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.