The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

KAG is a logical reasoning and Q&A framework based on the OpenSPG engine and large language models. It is used to build logical reasoning and Q&A solutions for vertical domain knowledge bases. KAG supports logical reasoning, multi-hop fact Q&A, and integrates knowledge and chunk mutual indexing structure, conceptual semantic reasoning, schema-constrained knowledge construction, and logical form-guided hybrid reasoning and retrieval. The framework includes kg-builder for knowledge representation and kg-solver for logical symbol-guided hybrid solving and reasoning engine. KAG aims to enhance LLM service framework in professional domains by integrating logical and factual characteristics of KGs.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems. It offers usability by making parallelizing training or inference code effortless with just a single line of change. It ensures ecological compatibility by serving as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. EasyDist decouples auto-parallel algorithms from specific frameworks and IRs, allowing for the development and benchmarking of different auto-parallel algorithms in a flexible manner. The architecture includes MetaOp, MetaIR, and the ShardCombine Algorithm for SPMD sharding rules without manual annotations.

Multilspy is a Python library developed for research purposes to facilitate the creation of language server clients for querying and obtaining results of static analyses from various language servers. It simplifies the process by handling server setup, communication, and configuration parameters, providing a common interface for different languages. The library supports features like finding function/class definitions, callers, completions, hover information, and document symbols. It is designed to work with AI systems like Large Language Models (LLMs) for tasks such as Monitor-Guided Decoding to ensure code generation correctness and boost compilability.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

AgentDojo is a dynamic environment designed to evaluate prompt injection attacks and defenses for large language models (LLM) agents. It provides a benchmark script to run different suites and tasks with specified LLM models, defenses, and attacks. The tool is under active development, and users can inspect the results through dedicated documentation pages and the Invariant Benchmark Registry.

Yalm (Yet Another Language Model) is an LLM inference implementation in C++/CUDA, emphasizing performance engineering, documentation, scientific optimizations, and readability. It is not for production use and has been tested on Mistral-v0.2 and Llama-3.2. Requires C++20-compatible compiler, CUDA toolkit, and LLM safetensor weights in huggingface format converted to .yalm file.

LOTUS (LLMs Over Tables of Unstructured and Structured Data) is a query engine that provides a declarative programming model and an optimized query engine for reasoning-based query pipelines over structured and unstructured data. It offers a simple and intuitive Pandas-like API with semantic operators for fast and easy LLM-powered data processing. The tool implements a semantic operator programming model, allowing users to write AI-based pipelines with high-level logic and leaving the rest of the work to the query engine. LOTUS supports various semantic operators like sem_map, sem_filter, sem_extract, sem_agg, sem_topk, sem_join, sem_sim_join, and sem_search, enabling users to perform tasks like mapping records, filtering data, aggregating records, and more. The tool also supports different model classes such as LM, RM, and Reranker for language modeling, retrieval, and reranking tasks respectively.

ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs). It offers modular trainer designs, simplified code structures, and integrated pipelines for creating and cleaning synthetic data, enabling users to enhance LLM capabilities like code generation and complex reasoning with greater efficiency and flexibility.

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as a repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which allows benchmarking of various existing and novel AI methods tailored for EO data.

MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for LLMs. It simplifies code analysis for researchers and can be seamlessly integrated into LLM training and application processes to enhance performance in a range of code-related tasks. The sandbox environment ensures safe code execution, the code analysis module offers comprehensive analysis reports, and the information integration module combines compilation feedback and analysis results for complex code-related tasks.

LeRobot is a state-of-the-art AI library for real-world robotics in PyTorch. It aims to provide models, datasets, and tools to lower the barrier to entry to robotics, focusing on imitation learning and reinforcement learning. LeRobot offers pretrained models, datasets with human-collected demonstrations, and simulation environments. It plans to support real-world robotics on affordable and capable robots. The library hosts pretrained models and datasets on the Hugging Face community page.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

A set of tools to visualize and interact with sequences of 3D data with cross-platform support on Windows, Linux, and macOS. It provides a native Python interface for loading and displaying SMPL[-H/-X], MANO, FLAME, STAR, and SUPR sequences in an interactive viewer. Users can render 3D data on top of images, edit SMPL sequences and poses, export screenshots and videos, and utilize a high-performance ModernGL-based rendering pipeline. The tool is designed for easy use and hacking, with features like headless mode, remote mode, animatable camera paths, and a built-in extensible GUI.

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

ChemicalQDevice innovates new LLM/LLM agent pharmaceutical industry applications regarding cancer drug cost containment, clinical decision support, cancer signaling pathways, bioprocess engineering, biosynthesis, characterization, or drug synthesis. OpenAI, Anthropic, Gemini, or xAI direct chat proprietary software are utilized to generate LLM reports and propose detailed solutions. AI governance is employed with relevant software implementations, model bias amplification mitigation, and generation traceability analyses.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

Fairlearn is a Python package designed to help developers assess and mitigate fairness issues in artificial intelligence (AI) systems. It provides mitigation algorithms and metrics for model assessment. Fairlearn focuses on two types of harms: allocation harms and quality-of-service harms. The package follows the group fairness approach, aiming to identify groups at risk of experiencing harms and ensuring comparable behavior across these groups. Fairlearn consists of metrics for assessing model impacts and algorithms for mitigating unfairness in various AI tasks under different fairness definitions.

OpenPromptInjection is an open-source toolkit for attacks and defenses in LLM-integrated applications, enabling easy implementation, evaluation, and extension of attacks, defenses, and LLMs. It supports various attack and defense strategies, including prompt injection, paraphrasing, retokenization, data prompt isolation, instructional prevention, sandwich prevention, perplexity-based detection, LLM-based detection, response-based detection, and know-answer detection. Users can create models, tasks, and apps to evaluate different scenarios. The toolkit currently supports PaLM2 and provides a demo for querying models with prompts. Users can also evaluate ASV for different scenarios by injecting tasks and querying models with attacked data prompts.

This repository contains slides, notebooks, and data for the Machine Learning University (MLU) Responsible AI class. The mission is to make Machine Learning accessible to everyone, covering widely used ML techniques and applying them to real-world problems. The class includes lectures, final projects, and interactive visuals to help users learn about Responsible AI and core ML concepts.

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

LLM Hallucination Index - RAG Special is a comprehensive evaluation of large language models (LLMs) focusing on context length and open vs. closed-source attributes. The index explores the impact of context length on model performance and tests the assumption that closed-source LLMs outperform open-source ones. It also investigates the effectiveness of prompting techniques like Chain-of-Note across different context lengths. The evaluation includes 22 models from various brands, analyzing major trends and declaring overall winners based on short, medium, and long context insights. Methodologies involve rigorous testing with different context lengths and prompting techniques to assess models' abilities in handling extensive texts and detecting hallucinations.

The 'llm-misinformation-survey' repository is dedicated to the survey on combating misinformation in the age of Large Language Models (LLMs). It explores the opportunities and challenges of utilizing LLMs to combat misinformation, providing insights into the history of combating misinformation, current efforts, and future outlook. The repository serves as a resource hub for the initiative 'LLMs Meet Misinformation' and welcomes contributions of relevant research papers and resources. The goal is to facilitate interdisciplinary efforts in combating LLM-generated misinformation and promoting the responsible use of LLMs in fighting misinformation.