TheoremExplainAgent is an AI system that generates long-form Manim videos to visually explain theorems, proving its deep understanding while uncovering reasoning flaws that text alone often hides. The codebase for the paper 'TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding' is available in this repository. It provides a tool for creating multimodal explanations for theorem understanding using AI technology.

Mem0 is a tool that provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications. It offers persistent memory for users, sessions, and agents, self-improving personalization, a simple API for easy integration, and cross-platform consistency. Users can store memories, retrieve memories, search for related memories, update memories, get the history of a memory, and delete memories using Mem0. It is designed to enhance AI experiences by enabling long-term memory storage and retrieval.

Autogen Studio is a Docker container that provides a platform for running automated tasks using OpenAI API. It allows users to easily set up and manage a workspace for generating content and performing various tasks. The container can be run standalone or using Docker Compose, with options to mount data volumes and configure environment variables. Autogen Studio simplifies the process of leveraging AI capabilities for automating tasks and content generation.

A sleek and powerful macOS client for Amazon Bedrock, bringing AI models to your desktop. It provides seamless interaction with multiple Amazon Bedrock models, real-time chat interface, easy model switching, support for various AI tasks, and native Dark Mode support. Built with SwiftUI for optimal performance and modern UI.

MLX-VLM is a package designed for running Vision LLMs on Mac systems using MLX. It provides a convenient way to install and utilize the package for processing large language models related to vision tasks. The tool simplifies the process of running LLMs on Mac computers, offering a seamless experience for users interested in leveraging MLX for vision-related projects.

Hub is an open-source, high-performance LLM gateway written in Rust. It serves as a smart proxy for LLM applications, centralizing control and tracing of all LLM calls and traces. Built for efficiency, it provides a single API to connect to any LLM provider. The tool is designed to be fast, efficient, and completely open-source under the Apache 2.0 license.

RWKV Runner is a project designed to simplify the usage of large language models by automating various processes. It provides a lightweight executable program and is compatible with the OpenAI API. Users can deploy the backend on a server and use the program as a client. The project offers features like model management, VRAM configurations, user-friendly chat interface, WebUI option, parameter configuration, model conversion tool, download management, LoRA Finetune, and multilingual localization. It can be used for various tasks such as chat, completion, composition, and model inspection.

GPUStack is an open-source GPU cluster manager designed for running large language models (LLMs). It supports a wide variety of hardware, scales with GPU inventory, offers lightweight Python package with minimal dependencies, provides OpenAI-compatible APIs, simplifies user and API key management, enables GPU metrics monitoring, and facilitates token usage and rate metrics tracking. The tool is suitable for managing GPU clusters efficiently and effectively.

Scikit-LLM is a tool that seamlessly integrates powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks. It allows users to leverage large language models for various text analysis applications within the familiar scikit-learn framework. The tool simplifies the process of incorporating advanced language processing capabilities into machine learning pipelines, enabling users to benefit from the latest advancements in natural language processing.

CatAI is a tool that allows users to run GGUF models on their computer with a chat UI. It serves as a local AI assistant inspired by Node-Llama-Cpp and Llama.cpp. The tool provides features such as auto-detecting programming language, showing original messages by clicking on user icons, real-time text streaming, and fast model downloads. Users can interact with the tool through a CLI that supports commands for installing, listing, setting, serving, updating, and removing models. CatAI is cross-platform and supports Windows, Linux, and Mac. It utilizes node-llama-cpp and offers a simple API for asking model questions. Additionally, developers can integrate the tool with node-llama-cpp@beta for model management and chatting. The configuration can be edited via the web UI, and contributions to the project are welcome. The tool is licensed under Llama.cpp's license.

Ichigo is a local real-time voice AI tool that uses an early fusion technique to extend a text-based LLM to have native 'listening' ability. It is an open research experiment with improved multiturn capabilities and the ability to refuse processing inaudible queries. The tool is designed for open data, open weight, on-device Siri-like functionality, inspired by Meta's Chameleon paper. Ichigo offers a web UI demo and Gradio web UI for users to interact with the tool. It has achieved enhanced MMLU scores, stronger context handling, advanced noise management, and improved multi-turn capabilities for a robust user experience.

Airflint is a tool designed to enforce best practices for all your Airflow Directed Acyclic Graphs (DAGs). It is currently in the alpha stage and aims to help users adhere to recommended practices when working with Airflow. Users can install Airflint from PyPI and integrate it into their existing Airflow environment to improve DAG quality. The tool provides rules for function-level imports and jinja template syntax usage, among others, to enhance the development process of Airflow DAGs.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

A fast, easy-to-use, open source AI constraint solver for software developers. OptaPlanner is a powerful tool that helps developers solve complex optimization problems by providing a constraint satisfaction solver. It allows users to model and solve planning and scheduling problems efficiently, improving decision-making processes and resource allocation. With OptaPlanner, developers can easily integrate optimization capabilities into their applications, leading to better performance and cost-effectiveness.

LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.