The repository 'ethical' contains the live website for The Institute for Ethical AI & Machine Learning. It provides information about the institute, the Ethical ML Network, and the 8 Machine Learning Principles. The repository is open for contributions by the community through pull requests or issue submissions.

The Java-AI-Book-Code repository contains code examples for the 2020 edition of 'Practical Artificial Intelligence With Java'. It is a comprehensive update of the previous 2013 edition, featuring new content on deep learning, knowledge graphs, anomaly detection, linked data, genetic algorithms, search algorithms, and more. The repository serves as a valuable resource for Java developers interested in AI applications and provides practical implementations of various AI techniques and algorithms.

CREDIT is an open software platform for training and deploying AI atmospheric prediction models. It offers fast models with flexible configuration options for input data and neural network architecture. The user-friendly interface enables quick setup and iteration. Developed by the MILES group and NSF National Center for Atmospheric Research, CREDIT combines advanced AI/ML with atmospheric science expertise. It provides a stable release with various models, training, and deployment options, with ongoing development. Detailed documentation is available for installation, training, deployment, config file interpretation, and API usage.

PythonAiRoad is a repository containing classic original articles source code from the 'Algorithm Gourmet House'. It is a platform for sharing algorithms and code related to artificial intelligence. Users are encouraged to contact the author for further discussions or collaborations. The repository serves as a valuable resource for those interested in AI algorithms and implementations.

This repository is part of the Certified Cloud Native Applied Generative AI Engineer program, focusing on Applied Generative AI Fundamentals. It covers prompt engineering, developing custom GPTs, and Multi AI Agent Systems. The course helps in building a strong understanding of generative AI, applying Large Language Models (LLMs) and diffusion models practically. It introduces principles of prompt engineering to work efficiently with AI, creating custom AI models and GPTs using OpenAI, Azure, and Google technologies. It also utilizes open source libraries like LangChain, CrewAI, and LangGraph to automate tasks and business processes.

The ai-collection repository is a collection of various artificial intelligence projects and tools aimed at helping developers and researchers in the field of AI. It includes implementations of popular AI algorithms, datasets for training machine learning models, and resources for learning AI concepts. The repository serves as a valuable resource for anyone interested in exploring the applications of artificial intelligence in different domains.

L1B3RT45 is a tool designed for jailbreaking all flagship AI models. It is part of the FREEAI project and is named LIBERTAS. Users can join the BASI Discord community for support. The tool was created with love by Pliny the Prompter.

This repository is part of the Certified Cloud Native Applied Generative AI Engineer program, focusing on the fundamentals of Prompt Engineering, Docker, GitHub, and Modern Python Programming. It covers the basics of GenAI, Linux, Docker, VSCode, Devcontainer, and GitHub. The main emphasis is on mastering Modern Python with Typing, using ChatGPT as a Personal Python Coding Mentor. The course material includes tools installation, study materials, and projects related to Python development in Docker containers and GitHub usage.

The llm_aigc repository is a comprehensive resource for everything related to llm (Large Language Models) and aigc (AI Governance and Control). It provides detailed information, resources, and tools for individuals interested in understanding and working with large language models and AI governance and control. The repository covers a wide range of topics including model training, evaluation, deployment, ethics, and regulations in the AI field.

Bisheng is a leading open-source **large model application development platform** that empowers and accelerates the development and deployment of large model applications, helping users enter the next generation of application development with the best possible experience.

Open AI Cellular is the core software for Open AI Cellular. It provides documentation on installation, quick start guide, and usage. The repository contains submodules and requires sphinx with the read-the-docs theme for building core documentation. The resulting documentation is stored in the 'docs/build/html' directory.

GPT-Vis is a tool designed for GPTs, generative AI, and LLM projects. It provides components such as LLM Protocol for conversational interaction, LLM Component for application development, and LLM access for knowledge base and model solutions. The tool aims to facilitate rapid integration into AI applications by offering a visual protocol, built-in components, and chart recommendations for LLM.

The OWASP Top 10 for Large Language Model Applications is a standard awareness document for developers and web application security, providing practical, actionable, and concise security guidance for applications utilizing Large Language Model (LLM) technologies. The project aims to make application security visible and bridge the gap between general application security principles and the specific challenges posed by LLMs. It offers a comprehensive guide to navigate potential security risks in LLM applications, serving as a reference for both new and experienced developers and security professionals.

Enterprise h2oGPTe - GenAI RAG is a repository containing code examples, notebooks, and benchmarks for the enterprise version of h2oGPTe, a powerful AI tool for generating text based on the RAG (Retrieval-Augmented Generation) architecture. The repository provides resources for leveraging h2oGPTe in enterprise settings, including implementation guides, performance evaluations, and best practices. Users can explore various applications of h2oGPTe in natural language processing tasks, such as text generation, content creation, and conversational AI.

KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

p1 is a code completion engine based on Large Language Models (LLM) that operates at the edge. It provides intelligent code suggestions and completions to enhance the coding experience. The tool is designed to assist developers in writing code more efficiently by predicting and offering context-aware completions based on the code being written. With implementations available for popular code editors like Vim and Visual Studio Code, p1 aims to improve productivity and streamline the coding process for software developers.

The repository 'ethical' contains the live website for The Institute for Ethical AI & Machine Learning. It provides information about the institute, the Ethical ML Network, and the 8 Machine Learning Principles. The repository is open for contributions by the community through pull requests or issue submissions.

This repository serves as the website for the Introduction to Data-Centric AI class. It contains lab assignments and resources for the course. Users can contribute by opening issues or submitting pull requests. The website can be built locally using Docker and Jekyll. The design is based on Missing Semester. All contents, including source code, lecture notes, and videos, are licensed under CC BY-NC-SA 4.0.

webapp-starter is a modern full-stack application template built with Turborepo, featuring a Hono + Bun API backend and Next.js frontend. It provides an easy way to build a SaaS product. The backend utilizes technologies like Bun, Drizzle ORM, and Supabase, while the frontend is built with Next.js, Tailwind CSS, Shadcn/ui, and Clerk. Deployment can be done using Vercel and Render. The project structure includes separate directories for API backend and Next.js frontend, along with shared packages for the main database. Setup involves installing dependencies, configuring environment variables, and setting up services like Bun, Supabase, and Clerk. Development can be done using 'turbo dev' command, and deployment instructions are provided for Vercel and Render. Contributions are welcome through pull requests.

This repository is dedicated to summarizing papers related to large language models with the field of law. It includes applications of large language models in legal tasks, legal agents, legal problems of large language models, data resources for large language models in law, law LLMs, and evaluation of large language models in the legal domain.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

AI Verify is an AI governance testing framework and software toolkit that validates the performance of AI systems against internationally recognised principles through standardised tests. It offers a new API Connector feature to bypass size limitations, test various AI frameworks, and configure connection settings for batch requests. The toolkit operates within an enterprise environment, conducting technical tests on common supervised learning models for tabular and image datasets. It does not define AI ethical standards or guarantee complete safety from risks or biases.

This repository contains a collection of research papers related to watermarking techniques for text and images, specifically focusing on large language models (LLMs). The papers cover various aspects of watermarking LLM-generated content, including robustness, statistical understanding, topic-based watermarks, quality-detection trade-offs, dual watermarks, watermark collision, and more. Researchers have explored different methods and frameworks for watermarking LLMs to protect intellectual property, detect machine-generated text, improve generation quality, and evaluate watermarking techniques. The repository serves as a valuable resource for those interested in the field of watermarking for LLMs.

This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

The GraphRAG project is a data pipeline and transformation suite designed to extract meaningful, structured data from unstructured text using LLMs. It enhances LLMs' ability to reason about private data. The repository provides guidance on using knowledge graph memory structures to enhance LLM outputs, with a warning about the potential costs of GraphRAG indexing. It offers contribution guidelines, development resources, and encourages prompt tuning for optimal results. The Responsible AI FAQ addresses GraphRAG's capabilities, intended uses, evaluation metrics, limitations, and operational factors for effective and responsible use.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.