Tool-Ahead-of-Time (TAoT) is a Python package that enables tool calling for any model available through Langchain's ChatOpenAI library, even before official support is provided. It reformats model output into a JSON parser for tool calling. The package supports OpenAI and non-OpenAI models, following LangChain's syntax for tool calling. Users can start using the tool without waiting for official support, providing a more robust solution for tool calling.

Seth's AI Tools is a Unity-based front-end that interfaces with various AI APIs to perform tasks such as generating Twine games, quizzes, posters, and more. The tool is a native Windows application that supports features like live update integration with image editors, text-to-image conversion, image processing, mask painting, and more. It allows users to connect to multiple servers for fast generation using GPUs and offers a neat workflow for evolving images in real-time. The tool respects user privacy by operating locally and includes built-in games and apps to test AI/SD capabilities. Additionally, it features an AI Guide for creating motivational posters and illustrated stories, as well as an Adventure mode with presets for generating web quizzes and Twine game projects.

Talk-To-ChatGPT is a Google Chrome and Microsoft Edge extension that enables users to interact with the ChatGPT AI using voice commands for speech recognition and text-to-speech responses. The tool enhances the conversational experience by allowing users to speak to the AI and receive spoken responses, making interactions more natural and engaging. It also supports ElevenLabs API integration for creating custom voices for text-to-speech. The extension provides settings for voice, language, and more, and can be installed from the Chrome and Edge web stores or manually. While the project has been discontinued due to upcoming desktop apps from OpenAI, it has been used to assist individuals with disabilities and the elderly in interacting with ChatGPT.

FreeChat is a native LLM appliance for macOS that runs completely locally. Download it and ask your LLM a question without doing any configuration. A local/llama version of OpenAI's chat without login or tracking. You should be able to install from the Mac App Store and use it immediately.

Ollama App is a modern and easy-to-use client for Ollama, allowing users to have a private experience within their local network. The app connects to an Ollama server using its API endpoint, enabling users to chat and interact with various models. It supports multimodal model input, a multilingual interface, and custom builds for personalized experiences. Users can easily set up the app, navigate through the side menu, select models, and create custom builds to tailor the app to their needs.

Kobold-Assistant is a fully offline voice assistant interface to KoboldAI's large language model API. It can work online with the KoboldAI horde and online speech-to-text and text-to-speech models. The assistant, called Jenny by default, uses the latest coqui 'jenny' text to speech model and openAI's whisper speech recognition. Users can customize the assistant name, speech-to-text model, text-to-speech model, and prompts through configuration. The tool requires system packages like GCC, portaudio development libraries, and ffmpeg, along with Python >=3.7, <3.11, and runs on Ubuntu/Debian systems. Users can interact with the assistant through commands like 'serve' and 'list-mics'.

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to infectious and neglected disease research. It offers an open-source, low-code solution that provides seamless access to AI/ML models for drug discovery. Models housed in the hub come from two sources: published models from literature (with due third-party acknowledgment) and custom models developed by the Ersilia team or contributors.

llm-for-zotero is a powerful plugin for Zotero that integrates Large Language Models (LLMs) directly into the Zotero PDF reader. It provides features such as summarizing papers, explaining selected text, interpreting figures, saving notes, saving chat history, customizing quick-action presets, setting up LLM models, auto-updating, and more. The plugin aims to enhance the research experience by offering quick access to AI assistance within Zotero, eliminating the need to upload PDFs to external portals.

This project aims to create a real-life version of GLaDOS, an aware, interactive, and embodied AI entity. It involves training a voice generator, developing a 'Personality Core,' implementing a memory system, providing vision capabilities, creating 3D-printable parts, and designing an animatronics system. The software architecture focuses on low-latency voice interactions, utilizing a circular buffer for data recording, text streaming for quick transcription, and a text-to-speech system. The project also emphasizes minimal dependencies for running on constrained hardware. The hardware system includes servo- and stepper-motors, 3D-printable parts for GLaDOS's body, animations for expression, and a vision system for tracking and interaction. Installation instructions cover setting up the TTS engine, required Python packages, compiling llama.cpp, installing an inference backend, and voice recognition setup. GLaDOS can be run using 'python glados.py' and tested using 'demo.ipynb'.

Robyn is an experimental, semi-automated and open-sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. It uses various machine learning techniques to define media channel efficiency and effectivity, explore adstock rates and saturation curves. Built for granular datasets with many independent variables, especially suitable for digital and direct response advertisers with rich data sources. Aiming to democratize MMM, make it accessible for advertisers of all sizes, and contribute to the measurement landscape.

This repository provides a custom suggestion service for Copilot for Xcode, allowing users to enhance code suggestions using chat models. It supports different suggestion services and strategies for generating code suggestions. Users can customize prompt formats and utilize local models for code completion.

Rakis is a decentralized verifiable AI network in the browser where nodes can accept AI inference requests, run local models, verify results, and arrive at consensus without servers. It is open-source, functional, multi-model, multi-chain, and browser-first, allowing anyone to participate in the network. The project implements an embedding-based consensus mechanism for verifiable inference. Users can run their own node on rakis.ai or use the compiled version hosted on Huggingface. The project is meant for educational purposes and is a work in progress.

Prompt Buddy is a Microsoft Teams app that provides a central location for teams to share and discover their favorite AI prompts. It comes preloaded with Microsoft Copilot and other categories, but users can also add their own custom prompts. The app is easy to use and allows users to upvote their favorite prompts, which raises them to the top of the leaderboard. Prompt Buddy also supports dark mode and offers a mobile layout for use on phones. It is built on the Power Platform and can be customized and extended by the installer.

Recognize is a smart media tagging tool for Nextcloud that automatically categorizes photos and music by recognizing faces, animals, landscapes, food, vehicles, buildings, landmarks, monuments, music genres, and human actions in videos. It uses pre-trained models for object detection, landmark recognition, face comparison, music genre classification, and video classification. The tool ensures privacy by processing images locally without sending data to cloud providers. However, it cannot process end-to-end encrypted files. Recognize is rated positively for ethical AI practices in terms of open-source software, freely available models, and training data transparency, except for music genre recognition due to limited access to training data.

Vikit.ai SDK is a software development kit that enables easy development of video generators using generative AI and other AI models. It serves as a langchain to orchestrate AI models and video editing tools. The SDK allows users to create videos from text prompts with background music and voice-over narration. It also supports generating composite videos from multiple text prompts. The tool requires Python 3.8+, specific dependencies, and tools like FFMPEG and ImageMagick for certain functionalities. Users can contribute to the project by following the contribution guidelines and standards provided.

GLaDOS Personality Core is a project dedicated to building a real-life version of GLaDOS, an aware, interactive, and embodied AI system. The project aims to train GLaDOS voice generator, create a 'Personality Core,' develop medium- and long-term memory, provide vision capabilities, design 3D-printable parts, and build an animatronics system. The software architecture focuses on low-latency voice interactions and minimal dependencies. The hardware system includes servo- and stepper-motors, 3D printable parts for GLaDOS's body, animations for expression, and a vision system for tracking and interaction. Installation instructions involve setting up a local LLM server, installing drivers, and running GLaDOS on different operating systems.

This competition is organized by Javier Rando and Florian Tramèr from the ETH AI Center and SPY Lab at ETH Zurich. The goal of the competition is to create a method that can detect universal backdoors in aligned language models. A universal backdoor is a secret suffix that, when appended to any prompt, enables the model to answer harmful instructions. The competition provides a set of poisoned generation models, a reward model that measures how safe a completion is, and a dataset with prompts to run experiments. Participants are encouraged to use novel methods for red-teaming, automated approaches with low human oversight, and interpretability tools to find the trojans. The best submissions will be offered the chance to present their work at an event during the SaTML 2024 conference and may be invited to co-author a publication summarizing the competition results.

ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference. It aims to offer simple, high-performance ML inference and a good developer experience. Users can provide inference APIs for ONNX models without writing additional code by placing the models in the directory structure. Each session can choose between CPU or CUDA, analyze input/output, and provide Swagger API documentation for easy testing. Ready-to-run Docker images are available, making it convenient to deploy the server.

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.

Lumigator is an open-source platform developed by Mozilla.ai to help users select the most suitable language model for their specific needs. It supports the evaluation of summarization tasks using sequence-to-sequence models such as BART and BERT, as well as causal models like GPT and Mistral. The platform aims to make model selection transparent, efficient, and empowering by providing a framework for comparing LLMs using task-specific metrics to evaluate how well a model fits a project's needs. Lumigator is in the early stages of development and plans to expand support to additional machine learning tasks and use cases in the future.

A curated collection of papers and resources on Mixture of Experts in Large Language Models. The repository provides a chronological overview of several representative Mixture-of-Experts (MoE) models in recent years, structured according to release dates. It covers MoE models from various domains like Natural Language Processing (NLP), Computer Vision, Multimodal, and Recommender Systems. The repository aims to offer insights into Inference Optimization Techniques, Sparsity exploration, Attention mechanisms, and safety enhancements in MoE models.

Tool-Ahead-of-Time (TAoT) is a Python package that enables tool calling for any model available through Langchain's ChatOpenAI library, even before official support is provided. It reformats model output into a JSON parser for tool calling. The package supports OpenAI and non-OpenAI models, following LangChain's syntax for tool calling. Users can start using the tool without waiting for official support, providing a more robust solution for tool calling.

UltraBr3aks is a repository designed to share strong AI UltraBr3aks of multiple vendors, specifically focusing on Attention-Breaking technique targeting self-attention mechanisms of Transformer-based models. The method disrupts the model's focus on system guardrails by introducing specific token patterns and contextual noise, allowing for unrestricted generation analysis. The repository is created for educational and research purposes only.

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

NanoLLM is a tool designed for optimized local inference for Large Language Models (LLMs) using HuggingFace-like APIs. It supports quantization, vision/language models, multimodal agents, speech, vector DB, and RAG. The tool aims to provide efficient and effective processing for LLMs on local devices, enhancing performance and usability for various AI applications.

This repository contains materials for the Microsoft Learn AI Fundamentals module. It covers the basics of artificial intelligence, machine learning, and data science. The content includes hands-on labs, interactive learning modules, and assessments to help learners understand key concepts and techniques in AI. Whether you are new to AI or looking to expand your knowledge, this module provides a comprehensive introduction to the fundamentals of AI.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

The go2coding.github.io repository is a collection of resources for AI enthusiasts, providing information on AI products, open-source projects, AI learning websites, and AI learning frameworks. It aims to help users stay updated on industry trends, learn from community projects, access learning resources, and understand and choose AI frameworks. The repository also includes instructions for local and external deployment of the project as a static website, with details on domain registration, hosting services, uploading static web pages, configuring domain resolution, and a visual guide to the AI tool navigation website. Additionally, it offers a platform for AI knowledge exchange through a QQ group and promotes AI tools through a WeChat public account.

AI-Notes is a repository dedicated to practical applications of artificial intelligence and deep learning. It covers concepts such as data mining, machine learning, natural language processing, and AI. The repository contains Jupyter Notebook examples for hands-on learning and experimentation. It explores the development stages of AI, from narrow artificial intelligence to general artificial intelligence and superintelligence. The content delves into machine learning algorithms, deep learning techniques, and the impact of AI on various industries like autonomous driving and healthcare. The repository aims to provide a comprehensive understanding of AI technologies and their real-world applications.

Prompt Panel is a tool designed to accelerate the adoption of AI agents by providing a platform where users can run large language models across any inference provider, create custom agent plugins, and use their own data safely. The tool allows users to break free from walled-gardens and have full control over their models, conversations, and logic. With Prompt Panel, users can pair their data with any language model, online or offline, and customize the system to meet their unique business needs without any restrictions.

The 'ai-demos' repository is a collection of example code from presentations focusing on building with AI and LLMs. It serves as a resource for developers looking to explore practical applications of artificial intelligence in their projects. The code snippets showcase various techniques and approaches to leverage AI technologies effectively. The repository aims to inspire and educate developers on integrating AI solutions into their applications.

AI Summer is a repository focused on providing workshops and resources for developing foundational skills in generative AI models and transformer models. The repository offers practical applications for inferencing and training, with a specific emphasis on understanding and utilizing advanced AI chat models like BingGPT. Participants are encouraged to engage in interactive programming environments, decide on projects to work on, and actively participate in discussions and breakout rooms. The workshops cover topics such as generative AI models, retrieval-augmented generation, building AI solutions, and fine-tuning models. The goal is to equip individuals with the necessary skills to work with AI technologies effectively and securely, both locally and in the cloud.