Popular GitHub Repositories for Functional Safety

A curated list of awesome responsible machine learning resources.

? A refreshing functional take on deep learning, compatible with your favorite libraries

An open-source data logging library for machine learning models and data pipelines. ? Provides visibility into data quality & model performance over time. ?️ Supports privacy-preserving data collection, ensuring safety & robustness. ?

Fully functional Pokerbot that works on PartyPoker, PokerStars and GGPoker, scraping tables with Open-CV (adaptable via gui) or neural network and making decisions based on a genetic algorithm and montecarlo simulation for poker equity calculation. Binaries can be downloaded with this link:

A reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.).

fklearn: Functional Machine Learning

Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback

Secrets of RLHF in Large Language Models Part I: PPO

Corruption and Perturbation Robustness (ICLR 2019)

Chinese safety prompts for evaluating and improving the safety of LLMs. 中文安全prompts，用于评估和提升大模型的安全性。

JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

? LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). ? Extracts signals from prompts & responses, ensuring safety & security. ?? Features include text quality, relevance metrics, & sentiment analysis. ? A comprehensive tool for LLM observability. ?

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, exciting jailbreak methods on LLMs. It contains papers, codes, datasets, evaluations, and analyses.

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

A simple library for creating complex neural networks

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

The repository is for safe reinforcement learning baselines.

A framework to evaluate the generalization capability of safety alignment for LLMs

DiffSharp: Differentiable Functional Programming