This custom component for Home Assistant adds support for controlling Midea air conditioner and dehumidifier appliances via the local area network. It provides integration for various Midea appliances, allowing users to control settings such as humidity levels, fan speed, and more through Home Assistant. The component supports multiple protocols and entities for different appliance models, offering a comprehensive solution for managing Midea appliances on the local network.

MIMIR is a Python package designed for measuring memorization in Large Language Models (LLMs). It provides functionalities for conducting experiments related to membership inference attacks on LLMs. The package includes implementations of various attacks such as Likelihood, Reference-based, Zlib Entropy, Neighborhood, Min-K% Prob, Min-K%++, Gradient Norm, and allows users to extend it by adding their own datasets and attacks.

HugeSCM is a cloud-based version control system designed to address R&D repository size issues. It effectively manages large repositories and individual large files by separating data storage and utilizing advanced algorithms and data structures. It aims for optimal performance in handling version control operations of large-scale repositories, making it suitable for single large library R&D, AI model development, and game or driver development.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

torchchat is a codebase showcasing the ability to run large language models (LLMs) seamlessly. It allows running LLMs using Python in various environments such as desktop, server, iOS, and Android. The tool supports running models via PyTorch, chatting, generating text, running chat in the browser, and running models on desktop/server without Python. It also provides features like AOT Inductor for faster execution, running in C++ using the runner, and deploying and running on iOS and Android. The tool supports popular hardware and OS including Linux, Mac OS, Android, and iOS, with various data types and execution modes available.

Windows Agent Arena (WAA) is a scalable Windows AI agent platform designed for testing and benchmarking multi-modal, desktop AI agents. It provides researchers and developers with a reproducible and realistic Windows OS environment for AI research, enabling testing of agentic AI workflows across various tasks. WAA supports deploying agents at scale using Azure ML cloud infrastructure, allowing parallel running of multiple agents and delivering quick benchmark results for hundreds of tasks in minutes.

FastTrackML is an experiment tracking server focused on speed and scalability, fully compatible with MLFlow. It provides a user-friendly interface to track and visualize your machine learning experiments, making it easy to compare different models and identify the best performing ones. FastTrackML is open source and can be easily installed and run with pip or Docker. It is also compatible with the MLFlow Python package, making it easy to integrate with your existing MLFlow workflows.

The Frame Firmware & RTL Codebase is a comprehensive repository containing code for the Frame hardware system architecture. It includes sections for nRF52 Application, nRF52 Bootloader, and FPGA RTL. The nRF52 handles system operation, Lua scripting, Bluetooth networking, AI tasks, and power management, while the FPGA accelerates graphics and camera processing. The repository provides instructions for firmware development, debugging in VSCode, and FPGA development using tools like ARM GCC Toolchain, nRF Command Line Tools, Yosys, Project Oxide, and nextpnr. Users can build and flash projects for nRF52840 DK, modify FPGA RTL, and access pre-built accelerators bundled in the repo.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

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.

Hal9 is a tool that allows users to create and deploy generative applications such as chatbots and APIs quickly. It is open, intuitive, scalable, and powerful, enabling users to use various models and libraries without the need to learn complex app frameworks. With a focus on AI tasks like RAG, fine-tuning, alignment, and training, Hal9 simplifies the development process by skipping engineering tasks like frontend development, backend integration, deployment, and operations.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

sdkit (stable diffusion kit) is an easy-to-use library for utilizing Stable Diffusion in AI Art projects. It includes features like ControlNets, LoRAs, Textual Inversion Embeddings, GFPGAN, CodeFormer for face restoration, RealESRGAN for upscaling, k-samplers, support for custom VAEs, NSFW filter, model-downloader, parallel GPU support, and more. It offers a model database, auto-scanning for malicious models, and various optimizations. The API consists of modules for loading models, generating images, filters, model merging, and utilities, all managed through the sdkit.Context object.

This minimalistic UI serves as a simple interface for Ollama models, enabling real-time interaction with Local Language Models (LLMs). Users can chat with models, switch between different LLMs, save conversations, and create parameter-driven prompt templates. The tool is built using React, Next.js, and Tailwind CSS, with seamless integration with LangchainJs and Ollama for efficient model switching and context storage.

OllamaSharp is a .NET binding for the Ollama API, providing an intuitive API client to interact with Ollama. It offers support for all Ollama API endpoints, real-time streaming, progress reporting, and an API console for remote management. Users can easily set up the client, list models, pull models with progress feedback, stream completions, and build interactive chats. The project includes a demo console for exploring and managing the Ollama host.

Garak is a vulnerability scanner designed for LLMs (Large Language Models) that checks for various weaknesses such as hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks. It combines static, dynamic, and adaptive probes to explore vulnerabilities in LLMs. Garak is a free tool developed for red-teaming and assessment purposes, focusing on making LLMs or dialog systems fail. It supports various LLM models and can be used to assess their security and robustness.

This custom component for Home Assistant adds support for controlling Midea air conditioner and dehumidifier appliances via the local area network. It provides integration for various Midea appliances, allowing users to control settings such as humidity levels, fan speed, and more through Home Assistant. The component supports multiple protocols and entities for different appliance models, offering a comprehensive solution for managing Midea appliances on the local network.

ESPHome yaml files for AirGradient devices to maintain the research and accuracy of AirGradient sensors, while also gaining the benefits of ESPHome/HomeAssistant for easy to use switches, buttons, configurations, and dashboards. Maintains the ability to also send data to the AirGradient Dashboard, which can also be disabled/removed to keep all data local.

AirSonos is a Home Assistant Community Add-on that provides AirPlay capabilities for Sonos (and UPnP) players. It bridges the compatibility gap between Apple devices using AirPlay and Sonos players by creating virtual AirPlay devices for Sonos players in the network. The add-on may also work for other UPnP players like newer Samsung televisions. It is based on the AirConnect project, offering a solution for streaming audio to Sonos devices.

aiohomekit is a Python library that implements the HomeKit protocol for controlling HomeKit accessories using asyncio. It is primarily used with Home Assistant, targeting the same versions of Python and following their code standards. The library is still under development and does not offer API guarantees yet. It aims to match the behavior of real HAP controllers, even when not strictly specified, and works around issues like JSON formatting, boolean encoding, header sensitivity, and TCP packet splitting. aiohomekit is primarily tested with Phillips Hue and Eve Extend bridges via Home Assistant, but is known to work with many more devices. It does not support BLE accessories and is intended for client-side use only.

Frigate Home Assistant Integration provides a rich media browser with thumbnails and navigation, sensor entities for camera FPS, detection FPS, process FPS, skipped FPS, and objects detected, binary sensor entities for object motion, camera entities for live view and object detected snapshot, switch entities for clips, detection, snapshots, and improve contrast, and support for multiple Frigate instances. It offers easy installation via HACS and manual installation options for advanced users. Users need to configure the `mqtt` integration for Frigate to work. Additionally, media browsing and a companion Lovelace card are available for enhanced user experience. Refer to the main Frigate documentation for detailed installation instructions and usage guidance.

This repository contains a custom component for Home Assistant that integrates various Xiaomi Mi Air Purifier and Xiaomi Mi Air Humidifier models. It provides detailed support for different devices, including power control, preset modes, child lock, LED control, favorite level adjustment, and various attributes monitoring. The custom component offers a more extensive range of supported devices compared to the official Home Assistant component, with additional features and device compatibility. Users can easily set up and configure their Xiaomi air purifiers and humidifiers within Home Assistant for enhanced control and monitoring.

This custom component for Home Assistant adds support for controlling Midea air conditioner and dehumidifier appliances via the local area network. It provides integration for various Midea appliances, allowing users to control settings such as humidity levels, fan speed, and more through Home Assistant. The component supports multiple protocols and entities for different appliance models, offering a comprehensive solution for managing Midea appliances on the local network.

Protofy is a full-stack, batteries-included low-code enabled web/app and IoT system with an API system and real-time messaging. It is based on Protofy (protoflow + visualui + protolib + protodevices) + Expo + Next.js + Tamagui + Solito + Express + Aedes + Redbird + Many other amazing packages. Protofy can be used to fast prototype Apps, webs, IoT systems, automations, or APIs. It is a ultra-extensible CMS with supercharged capabilities, mobile support, and IoT support (esp32 thanks to esphome).

OpenMeter is a real-time and scalable usage metering tool for AI, usage-based billing, infrastructure, and IoT use cases. It provides a REST API for integrations and offers client SDKs in Node.js, Python, Go, and Web. OpenMeter is licensed under the Apache 2.0 License.

AIOsense is an all-in-one sensor that is modular, affordable, and easy to solder. It is designed to be an alternative to commercially available sensors and focuses on upgradeability. AIOsense is cheaper and better than most commercial sensors and supports a variety of sensors and modules, including: - (RGB)-LED - Barometer - Breath VOC equivalent - Buzzer / Beeper - CO² equivalent - Humidity sensor - Light / Illumination sensor - PIR motion sensor - Temperature sensor - mmWave / Radar sensor Upcoming features include full voice assistant support, microphone, and speaker. All supported sensors & modules are listed in the documentation. AIOsense has a low power consumption, with an idle power consumption of 0.45W / 0.09A on a fully equipped board. Without a mmWave sensor, the idle power consumption is around 0.11W / 0.02A. To get started with AIOsense, you can refer to the documentation. If you have any questions, you can open an issue.