RKLLama is a server and client tool designed for running and interacting with LLM models optimized for Rockchip RK3588(S) and RK3576 platforms. It allows models to run on the NPU, with features such as running models on NPU, partial Ollama API compatibility, pulling models from Huggingface, API REST with documentation, dynamic loading/unloading of models, inference requests with streaming modes, simplified model naming, CPU model auto-detection, and optional debug mode. The tool supports Python 3.8 to 3.12 and has been tested on Orange Pi 5 Pro and Orange Pi 5 Plus with specific OS versions.

MCP for Unity is a tool that acts as a bridge, enabling AI assistants to interact with the Unity Editor via a local MCP Client. Users can instruct their LLM to manage assets, scenes, scripts, and automate tasks within Unity. The tool offers natural language control, powerful tools for asset management, scene manipulation, and automation of workflows. It is extensible and designed to work with various MCP Clients, providing a range of functions for precise text edits, script management, GameObject operations, and more.

TranslateBookWithLLM is a Python application designed for large-scale text translation, such as entire books (.EPUB), subtitle files (.SRT), and plain text. It leverages local LLMs via the Ollama API or Gemini API. The tool offers both a web interface for ease of use and a command-line interface for advanced users. It supports multiple format translations, provides a user-friendly browser-based interface, CLI support for automation, multiple LLM providers including local Ollama models and Google Gemini API, and Docker support for easy deployment.

Zotero MCP is an open-source project that integrates AI capabilities with Zotero using the Model Context Protocol. It consists of a Zotero plugin and an MCP server, enabling AI assistants to search, retrieve, and cite references from Zotero library. The project features a unified architecture with an integrated MCP server, eliminating the need for a separate server process. It provides features like intelligent search, detailed reference information, filtering by tags and identifiers, aiding in academic tasks such as literature reviews and citation management.

Zero is an open-source AI email solution that allows users to self-host their email app while integrating external services like Gmail. It aims to modernize and enhance emails through AI agents, offering features like open-source transparency, AI-driven enhancements, data privacy, self-hosting freedom, unified inbox, customizable UI, and developer-friendly extensibility. Built with modern technologies, Zero provides a reliable tech stack including Next.js, React, TypeScript, TailwindCSS, Node.js, Drizzle ORM, and PostgreSQL. Users can set up Zero using standard setup or Dev Container setup for VS Code users, with detailed environment setup instructions for Better Auth, Google OAuth, and optional GitHub OAuth. Database setup involves starting a local PostgreSQL instance, setting up database connection, and executing database commands for dependencies, tables, migrations, and content viewing.

G4f.dev is the official documentation hub for GPT4Free, a free and convenient AI tool with endpoints that can be integrated directly into apps, scripts, and web browsers. The documentation provides clear overviews, quick examples, and deeper insights into the major features of GPT4Free, including text and image generation. Users can choose between Python and JavaScript for installation and setup, and can access various API endpoints, providers, models, and client options for different tasks.

Probe is an AI-friendly, fully local, semantic code search tool designed to power the next generation of AI coding assistants. It combines the speed of ripgrep with the code-aware parsing of tree-sitter to deliver precise results with complete code blocks, making it perfect for large codebases and AI-driven development workflows. Probe supports various features like AI-friendly code extraction, fully local operation without external APIs, fast scanning of large codebases, accurate code structure parsing, re-rankers and NLP methods for better search results, multi-language support, interactive AI chat mode, and flexibility to run as a CLI tool, MCP server, or interactive AI chat.

Swift OCR is a powerful tool for extracting text from PDF files using OpenAI's GPT-4 Turbo with Vision model. It offers flexible input options, advanced OCR processing, performance optimizations, structured output, robust error handling, and scalable architecture. The tool ensures accurate text extraction, resilience against failures, and efficient handling of multiple requests.

PAELLADOC is an intelligent documentation system that uses AI to analyze code repositories and generate comprehensive technical documentation. It offers a modular architecture with MECE principles, interactive documentation process, key features like Orchestrator and Commands, and a focus on context for successful AI programming. The tool aims to streamline documentation creation, code generation, and product management tasks for software development teams, providing a definitive standard for AI-assisted development documentation.

Probe is an AI-friendly, fully local, semantic code search tool designed to power the next generation of AI coding assistants. It combines the speed of ripgrep with the code-aware parsing of tree-sitter to deliver precise results with complete code blocks, making it perfect for large codebases and AI-driven development workflows. Probe is fully local, keeping code on the user's machine without relying on external APIs. It supports multiple languages, offers various search options, and can be used in CLI mode, MCP server mode, AI chat mode, and web interface. The tool is designed to be flexible, fast, and accurate, providing developers and AI models with full context and relevant code blocks for efficient code exploration and understanding.

A powerful Go framework for building intelligent agent systems with large language models (LLMs), hierarchical planners, memory, telemetry, and a rich tool ecosystem. tRPC-Agent-Go enables the creation of autonomous or semi-autonomous agents that reason, call tools, collaborate with sub-agents, and maintain long-term state. The framework provides detailed documentation, examples, and tools for accelerating the development of AI applications.

AIPex is a revolutionary Chrome extension that transforms your browser into an intelligent automation platform. Using natural language commands and AI-powered intelligence, AIPex can automate virtually any browser task - from complex multi-step workflows to simple repetitive actions. It offers features like natural language control, AI-powered intelligence, multi-step automation, universal compatibility, smart data extraction, precision actions, form automation, visual understanding, developer-friendly with extensive API, and lightning-fast execution of automation tasks.

This project implements a web API that offers a unified interface to Google Gemini and Claude 3. It provides a self-hosted, lightweight, and scalable solution for accessing these AI models through a streaming API. The API supports both Claude and Gemini models, allowing users to interact with them in real-time. The project includes a user-friendly web UI for configuration and documentation, making it easy to get started and explore the capabilities of the API.

LAYRA is the world's first visual-native AI automation engine that sees documents like a human, preserves layout and graphical elements, and executes arbitrarily complex workflows with full Python control. It empowers users to build next-generation intelligent systems with no limits or compromises. Built for Enterprise-Grade deployment, LAYRA features a modern frontend, high-performance backend, decoupled service architecture, visual-native multimodal document understanding, and a powerful workflow engine.

ebook2audiobook is a CPU/GPU converter tool that converts eBooks to audiobooks with chapters and metadata using tools like Calibre, ffmpeg, XTTSv2, and Fairseq. It supports voice cloning and a wide range of languages. The tool is designed to run on 4GB RAM and provides a new v2.0 Web GUI interface for user-friendly interaction. Users can convert eBooks to text format, split eBooks into chapters, and utilize high-quality text-to-speech functionalities. Supported languages include Arabic, Chinese, English, French, German, Hindi, and many more. The tool can be used for legal, non-DRM eBooks only and should be used responsibly in compliance with applicable laws.

The Neutone SDK is a tool designed for researchers to wrap their own audio models and run them in a DAW using the Neutone Plugin. It simplifies the process by allowing models to be built using PyTorch and minimal Python code, eliminating the need for extensive C++ knowledge. The SDK provides support for buffering inputs and outputs, sample rate conversion, and profiling tools for model performance testing. It also offers examples, notebooks, and a submission process for sharing models with the community.

RKLLama is a server and client tool designed for running and interacting with LLM models optimized for Rockchip RK3588(S) and RK3576 platforms. It allows models to run on the NPU, with features such as running models on NPU, partial Ollama API compatibility, pulling models from Huggingface, API REST with documentation, dynamic loading/unloading of models, inference requests with streaming modes, simplified model naming, CPU model auto-detection, and optional debug mode. The tool supports Python 3.8 to 3.12 and has been tested on Orange Pi 5 Pro and Orange Pi 5 Plus with specific OS versions.

LMQL is a programming language designed for large language models (LLMs) that offers a unique way of integrating traditional programming with LLM interaction. It allows users to write programs that combine algorithmic logic with LLM calls, enabling model reasoning capabilities within the context of the program. LMQL provides features such as Python syntax integration, rich control-flow options, advanced decoding techniques, powerful constraints via logit masking, runtime optimization, sync and async API support, multi-model compatibility, and extensive applications like JSON decoding and interactive chat interfaces. The tool also offers library integration, flexible tooling, and output streaming options for easy model output handling.

llm-gateway is a gateway tool designed for interacting with third-party LLM providers such as OpenAI, Cohere, etc. It tracks data exchanged with these providers in a postgres database, applies PII scrubbing heuristics, and ensures safe communication with OpenAI's services. The tool supports various models from different providers and offers API and Python usage examples. Developers can set up the tool using Poetry, Pyenv, npm, and yarn for dependency management. The project also includes Docker setup for backend and frontend development.

Ollamac is a macOS app designed for interacting with Ollama models. It is optimized for macOS, allowing users to easily use any model from the Ollama library. The app features a user-friendly interface, chat archive for saving interactions, and real-time communication using HTTP streaming technology. Ollamac is open-source, enabling users to contribute to its development and enhance its capabilities. It requires macOS 14 or later and the Ollama system to be installed on the user's Mac with at least one Ollama model downloaded.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

Mistral AI SDK for Kotlin Multiplatform (KMP) allows communication with Mistral API to get AI models, start a chat with the assistant, and create embeddings. The library is based on Mistral API documentation and built with Kotlin Multiplatform and Ktor client library. Sample projects like ZeChat showcase the capabilities of Mistral AI SDK. Users can interact with different Mistral AI models through ZeChat apps on Android, Desktop, and Web platforms. The library is not yet published on Maven, but users can fork the project and use it as a module dependency in their apps.

LightRAG is a PyTorch library designed for building and optimizing Retriever-Agent-Generator (RAG) pipelines. It follows principles of simplicity, quality, and optimization, offering developers maximum customizability with minimal abstraction. The library includes components for model interaction, output parsing, and structured data generation. LightRAG facilitates tasks like providing explanations and examples for concepts through a question-answering pipeline.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.