This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

Spatz-2 is a complete, fullstack template for Svelte, utilizing technologies such as Sveltekit, Pocketbase, OpenAI, Vercel AI SDK, TailwindCSS, svelte-animations, and Zod. It offers features like user authentication, admin dashboard, dark/light mode themes, AI chatbot, guestbook, and forms with client/server validation. The project structure includes components, stores, routes, APIs, and icons. Spatz-2 aims to provide a futuristic web framework for building fast web apps with advanced functionalities and easy customization.

LLMGA (Multimodal Large Language Model-based Generation Assistant) is a tool that leverages Large Language Models (LLMs) to assist users in image generation and editing. It provides detailed language generation prompts for precise control over Stable Diffusion (SD), resulting in more intricate and precise content in generated images. The tool curates a dataset for prompt refinement, similar image generation, inpainting & outpainting, and visual question answering. It offers a two-stage training scheme to optimize SD alignment and a reference-based restoration network to alleviate texture, brightness, and contrast disparities in image editing. LLMGA shows promising generative capabilities and enables wider applications in an interactive manner.

Twelvet is a permission management system based on Spring Cloud Alibaba that serves as a framework for rapid development. It is a scaffolding framework based on microservices architecture, aiming to reduce duplication of business code and provide a common core business code for both microservices and monoliths. It is designed for learning microservices concepts and development, suitable for website management, CMS, CRM, OA, and other system development. The system is intended to quickly meet business needs, improve user experience, and save time by incubating practical functional points in lightweight, highly portable functional plugins.

Awesome MCP Servers is a curated list of Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list includes production-ready and experimental servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

Spatz is a complete, fullstack template for Svelte that includes features such as Sveltekit for building fast web apps, Pocketbase for User Auth and Database, OpenAI for chatbots, Vercel AI SDK for AI/ML models, TailwindCSS for UI development, DaisyUI for components, and Zod for schema declaration and validation. The template provides a structured project setup with components, stores, routes, and APIs. It also offers theming and styling options with pre-loaded themes from DaisyUI. Contributions are welcomed through feature requests or pull requests.

RAGLAB is a modular, research-oriented open-source framework for Retrieval-Augmented Generation (RAG) algorithms. It offers reproductions of 6 existing RAG algorithms and a comprehensive evaluation system with 10 benchmark datasets, enabling fair comparisons between RAG algorithms and easy expansion for efficient development of new algorithms, datasets, and evaluation metrics. The framework supports the entire RAG pipeline, provides advanced algorithm implementations, fair comparison platform, efficient retriever client, versatile generator support, and flexible instruction lab. It also includes features like Interact Mode for quick understanding of algorithms and Evaluation Mode for reproducing paper results and scientific research.

AgentKit is a framework developed by Coinbase Developer Platform for enabling AI agents to take actions onchain. It is designed to be framework-agnostic and wallet-agnostic, allowing users to integrate it with any AI framework and any wallet. The tool is actively being developed and encourages community contributions. AgentKit provides support for various protocols, frameworks, wallets, and networks, making it versatile for blockchain transactions and API integrations using natural language inputs.

Open-R1 is an open-source library for training a hyper-personalized DeepSeek-R1-like model using minimal compute resources. It provides the flexibility to use your own data and aims to streamline the model training process. The project is licensed under Apache 2.0 and acknowledges contributions from various open-source contributors, including Hugging Face and Vicuna.

The Fire-Flyer File System (3FS) is a high-performance distributed file system designed for AI training and inference workloads. It leverages modern SSDs and RDMA networks to provide a shared storage layer that simplifies development of distributed applications. Key features include performance, disaggregated architecture, strong consistency, file interfaces, data preparation, dataloaders, checkpointing, and KVCache for inference. The system is well-documented with design notes, setup guide, USRBIO API reference, and P specifications. Performance metrics include peak throughput, GraySort benchmark results, and KVCache optimization. The source code is available on GitHub for cloning and installation of dependencies. Users can build 3FS and run test clusters following the provided instructions. Issues can be reported on the GitHub repository.

assistant-ui is a set of React components for AI chat, providing wide model provider support out of the box and the ability to integrate custom APIs. It includes integrations with Langchain, Vercel AI SDK, TailwindCSS, shadcn-ui, react-markdown, react-syntax-highlighter, React Hook Form, and more. The tool allows users to quickly create AI chat applications with pre-configured templates and easy setup steps.

Aura Design is an open-source AI-friendly component library for next-generation intelligent applications. It offers AI-optimized interactive components designed for seamless integration with AI model I/O. The library works with various web frameworks and allows easy customization to suit specific requirements. With a modern design and smart markdown renderer, Aura Design enhances the overall user experience.

CAD-Recode is a 3D CAD reverse engineering method implemented in Python using the CadQuery library. It transforms point clouds into 3D CAD models by leveraging a pre-trained model and additional linear layers. The repository includes an inference demo for users to generate CAD models from point clouds. CAD-Recode has achieved state-of-the-art performance in CAD reconstruction benchmarks such as DeepCAD, Fusion360, and CC3D. Researchers and engineers can utilize this tool to reverse engineer CAD code from point clouds efficiently.

This repository contains sample code demonstrating various use cases leveraging Amazon Bedrock and Generative AI. Each sample is a separate project with its own directory, and includes a basic Streamlit frontend to help users quickly set up a proof of concept.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

AXONE Smart Contracts repository hosts Smart Contracts for the AXONE network, compatible with any Cosmos blockchains using the CosmWasm framework. It includes storage, sovereignty, and resource management oriented Smart Contracts. Each contract has different functionalities and maturity stages, with detailed tech documentation and emojis indicating maturity levels. The repository provides tools for building, testing, deploying, and interacting with Smart Contracts, along with guidelines for contributing and community engagement.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

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...).

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.