SuperBench is a validation and profiling tool for AI infrastructure. It provides a comprehensive set of tests and benchmarks to evaluate the performance and reliability of AI systems. The tool helps users identify bottlenecks, optimize configurations, and ensure the stability of their AI infrastructure. SuperBench is designed to streamline the validation process and improve the overall efficiency of AI deployments.

Complexity is a community-driven, open-source, and free third-party extension that enhances the features of Perplexity.ai. It provides various UI/UX/QoL tweaks, LLM/Image gen model selectors, a customizable theme, and a prompts library. The tool intercepts network traffic to alter the behavior of the host page, offering a solution to the limitations of Perplexity.ai. Users can install Complexity from Chrome Web Store, Mozilla Add-on, or build it from the source code.

Txtai is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation, and more. Txtai can stand alone or serve as a knowledge source for large language models (LLMs). Key features include vector search with SQL, object storage, topic modeling, graph analysis, multimodal indexing, embedding creation for various data types, pipelines powered by language models, workflows to connect pipelines, and support for Python, JavaScript, Java, Rust, and Go. Txtai is open-source under the Apache 2.0 license.

Awesome Agents is a curated list of open source AI agents designed for various tasks such as private interactions with documents, chat implementations, autonomous research, human-behavior simulation, code generation, HR queries, domain-specific research, and more. The agents leverage Large Language Models (LLMs) and other generative AI technologies to provide solutions for complex tasks and projects. The repository includes a diverse range of agents for different use cases, from conversational chatbots to AI coding engines, and from autonomous HR assistants to vision task solvers.

Lagent is a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents. It also provides some typical tools to augment LLM. The overview of our framework is shown below:

MaxKB is a knowledge base Q&A system based on the LLM large language model. MaxKB = Max Knowledge Base, which aims to become the most powerful brain of the enterprise.

SuperCoder is an open-source autonomous software development system that leverages advanced AI tools and agents to streamline and automate coding, testing, and deployment tasks, enhancing efficiency and reliability. It supports a variety of languages and frameworks for diverse development needs. Users can set up the environment variables, build and run the Go server, Asynq worker, and Postgres using Docker and Docker Compose. The project is under active development and may still have issues, but users can seek help and support from the Discord community or by creating new issues on GitHub.

Aiken is a modern smart contract platform for Cardano, providing a user-friendly environment for developing and deploying smart contracts. It supports Linux, MacOS, and Windows operating systems. Aiken is designed to simplify the process of creating smart contracts on the Cardano blockchain, offering a seamless experience for developers. The platform is named after Howard Aiken, an American physicist and computing pioneer.

Olares is an open-source sovereign cloud OS designed for local AI, enabling users to build their own AI assistants, sync data across devices, self-host their workspace, stream media, and more within a sovereign cloud environment. Users can effortlessly run leading AI models, deploy open-source AI apps, access AI apps and models anywhere, and benefit from integrated AI for personalized interactions. Olares offers features like edge AI, personal data repository, self-hosted workspace, private media server, smart home hub, and user-owned decentralized social media. The platform provides enterprise-grade security, secure application ecosystem, unified file system and database, single sign-on, AI capabilities, built-in applications, seamless access, and development tools. Olares is compatible with Linux, Raspberry Pi, Mac, and Windows, and offers a wide range of system-level applications, third-party components and services, and additional libraries and components.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

Pipeline is a Python library designed for constructing computational flows for AI/ML models. It supports both development and production environments, offering capabilities for inference, training, and finetuning. The library serves as an interface to Mystic, enabling the execution of pipelines at scale and on enterprise GPUs. Users can also utilize this SDK with Pipeline Core on a private hosted cluster. The syntax for defining AI/ML pipelines is reminiscent of sessions in Tensorflow v1 and Flows in Prefect.

AutoFlow is an open source graph rag based knowledge base tool built on top of TiDB Vector and LlamaIndex and DSPy. It features a Perplexity-style Conversational Search page and an Embeddable JavaScript Snippet for easy integration into websites. The tool allows for comprehensive coverage and streamlined search processes through sitemap URL scraping.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

Palico AI is a tech stack designed for rapid iteration of LLM applications. It allows users to preview changes instantly, improve performance through experiments, debug issues with logs and tracing, deploy applications behind a REST API, and manage applications with a UI control panel. Users have complete flexibility in building their applications with Palico, integrating with various tools and libraries. The tool enables users to swap models, prompts, and logic easily using AppConfig. It also facilitates performance improvement through experiments and provides options for deploying applications to cloud providers or using managed hosting. Contributions to the project are welcomed, with easy ways to get involved by picking issues labeled as 'good first issue'.

MLC LLM is a high-performance universal deployment solution that allows native deployment of any large language models with native APIs with compiler acceleration. It supports a wide range of model architectures and variants, including Llama, GPT-NeoX, GPT-J, RWKV, MiniGPT, GPTBigCode, ChatGLM, StableLM, Mistral, and Phi. MLC LLM provides multiple sets of APIs across platforms and environments, including Python API, OpenAI-compatible Rest-API, C++ API, JavaScript API and Web LLM, Swift API for iOS App, and Java API and Android App.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

SuperBench is a validation and profiling tool for AI infrastructure. It provides a comprehensive set of tests and benchmarks to evaluate the performance and reliability of AI systems. The tool helps users identify bottlenecks, optimize configurations, and ensure the stability of their AI infrastructure. SuperBench is designed to streamline the validation process and improve the overall efficiency of AI deployments.

Meta Lingua is a minimal and fast LLM training and inference library designed for research. It uses easy-to-modify PyTorch components to experiment with new architectures, losses, and data. The codebase enables end-to-end training, inference, and evaluation, providing tools for speed and stability analysis. The repository contains essential components in the 'lingua' folder and scripts that combine these components in the 'apps' folder. Researchers can modify the provided templates to suit their experiments easily. Meta Lingua aims to lower the barrier to entry for LLM research by offering a lightweight and focused codebase.

Digma is a Continuous Feedback platform that provides code-level insights related to performance, errors, and usage during development. It empowers developers to own their code all the way to production, improving code quality and preventing critical issues. Digma integrates with OpenTelemetry traces and metrics to generate insights in the IDE, helping developers analyze code scalability, bottlenecks, errors, and usage patterns.

High Performance LLMs 2024 is a comprehensive course focused on building a high-performance Large Language Model (LLM) from scratch using Jax. The course covers various aspects such as training, inference, roofline analysis, compilation, sharding, profiling, and optimization techniques. Participants will gain a deep understanding of Jax and learn how to design high-performance computing systems that operate close to their physical limits.

Scalene is a high-performance CPU, GPU, and memory profiler for Python that provides detailed information and runs faster than many other profilers. It incorporates AI-powered proposed optimizations, allowing users to generate optimization suggestions by clicking on specific lines or regions of code. Scalene separates time spent in Python from native code, highlights hotspots, and identifies memory usage per line. It supports GPU profiling on NVIDIA-based systems and detects memory leaks. Users can generate reduced profiles, profile specific functions using decorators, and suspend/resume profiling for background processes. Scalene is available as a pip or conda package and works on various platforms. It offers features like profiling at the line level, memory trends, copy volume reporting, and leak detection.

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.