Beet is a collection of crates for authoring and running web pages, games and AI behaviors. It includes crates like `beet_flow` for scenes-as-control-flow bevy library, `beet_spatial` for spatial behaviors, `beet_ml` for machine learning, `beet_sim` for simulation tooling, `beet_rsx` for authoring tools for html and bevy, and `beet_router` for file-based router for web docs. The `beet` crate acts as a base crate that re-exports sub-crates based on feature flags, similar to the `bevy` crate structure.

Free Chat is a forked project from chatgpt-demo that allows users to deploy a chat application with various features. It provides branches for different functionalities like token-based message list trimming and usage demonstration of 'promplate'. Users can control the website through environment variables, including setting OpenAI API key, temperature parameter, proxy, base URL, and more. The project welcomes contributions and acknowledges supporters. It is licensed under MIT by Muspi Merol.

optillm is an OpenAI API compatible optimizing inference proxy implementing state-of-the-art techniques to enhance accuracy and performance of LLMs, focusing on reasoning over coding, logical, and mathematical queries. By leveraging additional compute at inference time, it surpasses frontier models across diverse tasks.

Large Language Models Play StarCraft II is a project that explores the capabilities of large language models (LLMs) in playing the game StarCraft II. The project introduces TextStarCraft II, a textual environment for the game, and a Chain of Summarization method for analyzing game information and making strategic decisions. Through experiments, the project demonstrates that LLM agents can defeat the built-in AI at a challenging difficulty level. The project provides benchmarks and a summarization approach to enhance strategic planning and interpretability in StarCraft II gameplay.

Agentic is a standard AI functions/tools library optimized for TypeScript and LLM-based apps, compatible with major AI SDKs. It offers a set of thoroughly tested AI functions that can be used with favorite AI SDKs without writing glue code. The library includes various clients for services like Bing web search, calculator, Clearbit data resolution, Dexa podcast questions, and more. It also provides compound tools like SearchAndCrawl and supports multiple AI SDKs such as OpenAI, Vercel AI SDK, LangChain, LlamaIndex, Firebase Genkit, and Dexa Dexter. The goal is to create minimal clients with strongly-typed TypeScript DX, composable AIFunctions via AIFunctionSet, and compatibility with major TS AI SDKs.

AIKit is a one-stop shop to quickly get started to host, deploy, build and fine-tune large language models (LLMs). AIKit offers two main capabilities: Inference: AIKit uses LocalAI, which supports a wide range of inference capabilities and formats. LocalAI provides a drop-in replacement REST API that is OpenAI API compatible, so you can use any OpenAI API compatible client, such as Kubectl AI, Chatbot-UI and many more, to send requests to open-source LLMs! Fine Tuning: AIKit offers an extensible fine tuning interface. It supports Unsloth for fast, memory efficient, and easy fine-tuning experience.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

Benchmark various LLM Structured Output frameworks like Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, Outlines, LMFormatEnforcer, etc on tasks like multi-label classification, named entity recognition, synthetic data generation. The tool provides benchmark results, methodology, instructions to run the benchmark, add new data, and add a new framework. It also includes a roadmap for framework-related tasks, contribution guidelines, citation information, and feedback request.

rwkv.cpp is a port of BlinkDL/RWKV-LM to ggerganov/ggml, supporting FP32, FP16, and quantized INT4, INT5, and INT8 inference. It focuses on CPU but also supports cuBLAS. The project provides a C library rwkv.h and a Python wrapper. RWKV is a large language model architecture with models like RWKV v5 and v6. It requires only state from the previous step for calculations, making it CPU-friendly on large context lengths. Users are advised to test all available formats for perplexity and latency on a representative dataset before serious use.

GraphRAG Visualizer is an application designed to visualize Microsoft GraphRAG artifacts by uploading parquet files generated from the GraphRAG indexing pipeline. Users can view and analyze data in 2D or 3D graphs, display data tables, search for specific nodes or relationships, and process artifacts locally for data security and privacy.

The ovos-installer is a simple and multilingual tool designed to install Open Voice OS and HiveMind using Bash, Whiptail, and Ansible. It supports various Linux distributions and provides an automated installation process. Users can easily start and stop services, update their Open Voice OS instance, and uninstall the tool if needed. The installer also allows for non-interactive installation through scenario files. It offers a user-friendly way to set up Open Voice OS on different systems.

AiOS is a tool for human pose and shape estimation, performing human localization and SMPL-X estimation in a progressive manner. It consists of body localization, body refinement, and whole-body refinement stages. Users can download datasets for evaluation, SMPL-X body models, and AiOS checkpoint. Installation involves creating a conda virtual environment, installing PyTorch, torchvision, Pytorch3D, MMCV, and other dependencies. Inference requires placing the video for inference and pretrained models in specific directories. Test results are provided for NMVE, NMJE, MVE, and MPJPE on datasets like BEDLAM and AGORA. Users can run scripts for AGORA validation, AGORA test leaderboard, and BEDLAM leaderboard. The tool acknowledges codes from MMHuman3D, ED-Pose, and SMPLer-X.

GenAIComps is an initiative aimed at building enterprise-grade Generative AI applications using a microservice architecture. It simplifies the scaling and deployment process for production, abstracting away infrastructure complexities. GenAIComps provides a suite of containerized microservices that can be assembled into a mega-service tailored for real-world Enterprise AI applications. The modular approach of microservices allows for independent development, deployment, and scaling of individual components, promoting modularity, flexibility, and scalability. The mega-service orchestrates multiple microservices to deliver comprehensive solutions, encapsulating complex business logic and workflow orchestration. The gateway serves as the interface for users to access the mega-service, providing customized access based on user requirements.

Crabml is a llama.cpp compatible AI inference engine written in Rust, designed for efficient inference on various platforms with WebGPU support. It focuses on running inference tasks with SIMD acceleration and minimal memory requirements, supporting multiple models and quantization methods. The project is hackable, embeddable, and aims to provide high-performance AI inference capabilities.

Ollama GUI is a web interface for ollama.ai, a tool that enables running Large Language Models (LLMs) on your local machine. It provides a user-friendly platform for chatting with LLMs and accessing various models for text generation. Users can easily interact with different models, manage chat history, and explore available models through the web interface. The tool is built with Vue.js, Vite, and Tailwind CSS, offering a modern and responsive design for seamless user experience.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

Beet is a collection of crates for authoring and running web pages, games and AI behaviors. It includes crates like `beet_flow` for scenes-as-control-flow bevy library, `beet_spatial` for spatial behaviors, `beet_ml` for machine learning, `beet_sim` for simulation tooling, `beet_rsx` for authoring tools for html and bevy, and `beet_router` for file-based router for web docs. The `beet` crate acts as a base crate that re-exports sub-crates based on feature flags, similar to the `bevy` crate structure.

Aiohue is an asynchronous library designed to control Philips Hue lights. It requires Python 3.10+ and utilizes asyncio and aiohttp. The library supports both V1 and V2 APIs of the Hue Bridge, with V2 API offering event-based updates to eliminate the need for polling. The contribution guidelines emphasize matching object hierarchy and property/method names with the Philips Hue API.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.