r2ai

r2ai is a tool designed to run a language model locally without internet access. It can be used to entertain users or assist in answering questions related to radare2 or reverse engineering. The tool allows users to prompt the language model, index large codebases, slurp file contents, embed the output of an r2 command, define different system-level assistant roles, set environment variables, and more. It is accessible as an r2lang-python plugin and can be scripted from various languages. Users can use different models, adjust query templates dynamically, load multiple models, and make them communicate with each other.

Aim is a command-line tool for downloading and uploading files with resume support. It supports various protocols including HTTP, FTP, SFTP, SSH, and S3. Aim features an interactive mode for easy navigation and selection of files, as well as the ability to share folders over HTTP for easy access from other devices. Additionally, it offers customizable progress indicators and output formats, and can be integrated with other commands through piping. Aim can be installed via pre-built binaries or by compiling from source, and is also available as a Docker image for platform-independent usage.

PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. It is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests. The tool provides secure and isolated operations in a sandboxed Docker environment, fully autonomous AI-powered agent for penetration testing steps, a suite of 20+ professional security tools, smart memory system for storing research results, web intelligence for gathering information, integration with external search systems, team delegation system, comprehensive monitoring and reporting, modern interface, API integration, persistent storage, scalable architecture, self-hosted solution, flexible authentication, and quick deployment through Docker Compose.

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with Large Language Models (LLMs) to provide accurate question-answering capabilities. It offers a streamlined RAG workflow for businesses of all sizes, enabling them to extract knowledge from unstructured data in various formats, including Word documents, slides, Excel files, images, and more. RAGFlow's key features include deep document understanding, template-based chunking, grounded citations with reduced hallucinations, compatibility with heterogeneous data sources, and an automated and effortless RAG workflow. It supports multiple recall paired with fused re-ranking, configurable LLMs and embedding models, and intuitive APIs for seamless integration with business applications.

Stable Diffusion WebUI Docker Image allows users to run Automatic1111 WebUI in a docker container locally or in the cloud. The images do not bundle models or third-party configurations, requiring users to use a provisioning script for container configuration. It supports NVIDIA CUDA, AMD ROCm, and CPU platforms, with additional environment variables for customization and pre-configured templates for Vast.ai and Runpod.io. The service is password protected by default, with options for version pinning, startup flags, and service management using supervisorctl.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

MockingBird is a toolbox designed for Mandarin speech synthesis using PyTorch. It supports multiple datasets such as aidatatang_200zh, magicdata, aishell3, and data_aishell. The toolbox can run on Windows, Linux, and M1 MacOS, providing easy and effective speech synthesis with pretrained encoder/vocoder models. It is webserver ready for remote calling. Users can train their own models or use existing ones for the encoder, synthesizer, and vocoder. The toolbox offers a demo video and detailed setup instructions for installation and model training.

AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology.

Bespoke Curator is an open-source tool for data curation and structured data extraction. It provides a Python library for generating synthetic data at scale, with features like programmability, performance optimization, caching, and integration with HuggingFace Datasets. The tool includes a Curator Viewer for dataset visualization and offers a rich set of functionalities for creating and refining data generation strategies.

runpod-worker-comfy is a serverless API tool that allows users to run any ComfyUI workflow to generate an image. Users can provide input images as base64-encoded strings, and the generated image can be returned as a base64-encoded string or uploaded to AWS S3. The tool is built on Ubuntu + NVIDIA CUDA and provides features like built-in checkpoints and VAE models. Users can configure environment variables to upload images to AWS S3 and interact with the RunPod API to generate images. The tool also supports local testing and deployment to Docker hub using Github Actions.

Markdown Translation BOT is a GitHub action that translates markdown files into multiple languages using various AI models. It supports markdown, markdown-jsx, and json files only. The action can be executed by individuals with write permissions to the repository, preventing API abuse by non-trusted parties. Users can set up the action by providing their API key and configuring the workflow settings. The tool allows users to create comments with specific commands to trigger translations and automatically generate pull requests or add translated files to existing pull requests. It supports multiple file translations and can interpret any language supported by GPT-4 or GPT-3.5.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

nano-GraphRAG is a simple, easy-to-hack implementation of GraphRAG that provides a smaller, faster, and cleaner version of the official implementation. It is about 800 lines of code, small yet scalable, asynchronous, and fully typed. The tool supports incremental insert, async methods, and various parameters for customization. Users can replace storage components and LLM functions as needed. It also allows for embedding function replacement and comes with pre-defined prompts for entity extraction and community reports. However, some features like covariates and global search implementation differ from the original GraphRAG. Future versions aim to address issues related to data source ID, community description truncation, and add new components.

pgvecto.rs is a Postgres extension written in Rust that provides vector similarity search functions. It offers ultra-low-latency, high-precision vector search capabilities, including sparse vector search and full-text search. With complete SQL support, async indexing, and easy data management, it simplifies data handling. The extension supports various data types like FP16/INT8, binary vectors, and Matryoshka embeddings. It ensures system performance with production-ready features, high availability, and resource efficiency. Security and permissions are managed through easy access control. The tool allows users to create tables with vector columns, insert vector data, and calculate distances between vectors using different operators. It also supports half-precision floating-point numbers for better performance and memory usage optimization.

Pipecat is an open-source framework designed for building generative AI voice bots and multimodal assistants. It provides code building blocks for interacting with AI services, creating low-latency data pipelines, and transporting audio, video, and events over the Internet. Pipecat supports various AI services like speech-to-text, text-to-speech, image generation, and vision models. Users can implement new services and contribute to the framework. Pipecat aims to simplify the development of applications like personal coaches, meeting assistants, customer support bots, and more by providing a complete framework for integrating AI services.

The Prompt Fuzzer is an open-source tool that helps you assess the security of your GenAI application's system prompt against various dynamic LLM-based attacks. It provides a security evaluation based on the outcome of these attack simulations, enabling you to strengthen your system prompt as needed. The Prompt Fuzzer dynamically tailors its tests to your application's unique configuration and domain. The Fuzzer also includes a Playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks.

r2ai is a tool designed to run a language model locally without internet access. It can be used to entertain users or assist in answering questions related to radare2 or reverse engineering. The tool allows users to prompt the language model, index large codebases, slurp file contents, embed the output of an r2 command, define different system-level assistant roles, set environment variables, and more. It is accessible as an r2lang-python plugin and can be scripted from various languages. Users can use different models, adjust query templates dynamically, load multiple models, and make them communicate with each other.

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_Web_search project gives local LLMs the ability to search the web by outputting a specific command. It uses regular expressions to extract search queries from model output and then utilizes duckduckgo-search to search the web. LangChain's Contextual compression and Okapi BM25 or SPLADE are used to extract relevant parts of web pages in search results. The extracted results are appended to the model's output.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.