TinyFT: Lightweight Fine-Tuning Library TinyFT is a lightweight, modular toy-scale library designed from scratch for fine-tuning LLMs. View on GitHub DP-ZeRO Trainer Differential Privacy experimentations, and implementation of Differential Privacy training with Zero Redundancy Optimizer support. View on GitHub Daily Quantitative Trading backtesting. A toy-scale CLI-based backtesting Quantitative Daily Trading system with support for various common trading strategies. View on GitHub