ClicShoppingAI is a powerful open-source Ecommerce solution that supports B2B, B2C, and B2B-B2C. Integrated with cutting-edge generative artificial intelligence systems like Gpt and Ollama, it helps merchants increase turnover and competitiveness for free. With AI capabilities, it optimizes inventory, offers personalized recommendations, and provides top-notch customer service. The solution is modular, lightweight, and user-friendly, with a seamless, responsive design for all devices. Installation is easy, empowering ongoing development through community support. Features include GPT API integration, generative AI functionalities, real-time safety stock predictive, WYSIWYG product description creation, image editor management, full SEO optimization, payment and shipping modules, extension system, GDPR compliance, multi-language support, and more.

Higress is an open-source cloud-native API gateway built on the core of Istio and Envoy, based on Alibaba's internal practice of Envoy Gateway. It is designed for AI-native API gateway, serving AI businesses such as Tongyi Qianwen APP, Bailian Big Model API, and Machine Learning PAI platform. Higress provides capabilities to interface with LLM model vendors, AI observability, multi-model load balancing/fallback, AI token flow control, and AI caching. It offers features for AI gateway, Kubernetes Ingress gateway, microservices gateway, and security protection gateway, with advantages in production-level scalability, stream processing, extensibility, and ease of use.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

Dapr Agents is a developer framework for building production-grade resilient AI agent systems that operate at scale. It enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while providing built-in observability and stateful workflow execution to ensure agentic workflows complete successfully. The framework is scalable, efficient, Kubernetes-native, data-driven, secure, observable, vendor-neutral, and open source. It offers features like scalable workflows, cost-effective AI adoption, data-centric AI agents, accelerated development, integrated security and reliability, built-in messaging and state infrastructure, and vendor-neutral and open source support. Dapr Agents is designed to simplify the development of AI applications and workflows by providing a comprehensive API surface and seamless integration with various data sources and services.

LLM-Zero-to-Hundred is a repository showcasing various applications of LLM chatbots and providing insights into training and fine-tuning Language Models. It includes projects like WebGPT, RAG-GPT, WebRAGQuery, LLM Full Finetuning, RAG-Master LLamaindex vs Langchain, open-source-RAG-GEMMA, and HUMAIN: Advanced Multimodal, Multitask Chatbot. The projects cover features like ChatGPT-like interaction, RAG capabilities, image generation and understanding, DuckDuckGo integration, summarization, text and voice interaction, and memory access. Tutorials include LLM Function Calling and Visualizing Text Vectorization. The projects have a general structure with folders for README, HELPER, .env, configs, data, src, images, and utils.

Sourcegraph is a tool that simplifies reading, writing, and fixing code in large and complex codebases. It offers features such as code search across repositories and hosts, code intelligence for navigation and references, and the ability to roll out large-scale changes and track migrations. Sourcegraph can be used on the cloud or self-hosted, with public code search available on Sourcegraph.com. The tool provides high-level architecture documentation, database setup best practices, Go and documentation style guides, tips for modifying the GraphQL API, and guidelines for contributing.

Sourcegraph is a tool that simplifies reading, writing, and fixing code in large and complex codebases. It offers features such as code search across repositories, code intelligence for code navigation and history tracing, and the ability to roll out large-scale changes to multiple repositories simultaneously. Sourcegraph can be used on the cloud or self-hosted, and provides public code search on Sourcegraph.com. The tool is designed to enhance code understanding and collaboration within development teams.

RLinf is a flexible and scalable open-source infrastructure designed for post-training foundation models via reinforcement learning. It provides a robust backbone for next-generation training, supporting open-ended learning, continuous generalization, and limitless possibilities in intelligence development. The tool offers unique features like Macro-to-Micro Flow, flexible execution modes, auto-scheduling strategy, embodied agent support, and fast adaptation for mainstream VLA models. RLinf is fast with hybrid mode and automatic online scaling strategy, achieving significant throughput improvement and efficiency. It is also flexible and easy to use with multiple backend integrations, adaptive communication, and built-in support for popular RL methods. The roadmap includes system-level enhancements and application-level extensions to support various training scenarios and models. Users can get started with complete documentation, quickstart guides, key design principles, example gallery, advanced features, and guidelines for extending the framework. Contributions are welcome, and users are encouraged to cite the GitHub repository and acknowledge the broader open-source community.

CORE is an open-source unified, persistent memory layer for all AI tools, allowing developers to maintain context across different tools like Cursor, ChatGPT, and Claude. It aims to solve the issue of context switching and information loss between sessions by creating a knowledge graph that remembers conversations, decisions, and insights. With features like unified memory, temporal knowledge graph, browser extension, chat with memory, auto-sync from apps, and MCP integration hub, CORE provides a seamless experience for managing and recalling context. The tool's ingestion pipeline captures evolving context through normalization, extraction, resolution, and graph integration, resulting in a dynamic memory that grows and changes with the user. When recalling from memory, CORE utilizes search, re-ranking, filtering, and output to provide relevant and contextual answers. Security measures include data encryption, authentication, access control, and vulnerability reporting.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

NeMo Curator is a Python library designed for fast and scalable data processing and curation for generative AI use cases. It accelerates data processing by leveraging GPUs with Dask and RAPIDS, providing customizable pipelines for text and image curation. The library offers pre-built pipelines for synthetic data generation, enabling users to train and customize generative AI models such as LLMs, VLMs, and WFMs.

Inngest is a platform that offers durable functions to replace queues, state management, and scheduling for developers. It allows writing reliable step functions faster without dealing with infrastructure. Developers can create durable functions using various language SDKs, run a local development server, deploy functions to their infrastructure, sync functions with the Inngest Platform, and securely trigger functions via HTTPS. Inngest Functions support retrying, scheduling, and coordinating operations through triggers, flow control, and steps, enabling developers to build reliable workflows with robust support for various operations.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

DocsGPT is an open-source documentation assistant powered by GPT models. It simplifies the process of searching for information in project documentation by allowing developers to ask questions and receive accurate answers. With DocsGPT, users can say goodbye to manual searches and quickly find the information they need. The tool aims to revolutionize project documentation experiences and offers features like live previews, Discord community, guides, and contribution opportunities. It consists of a Flask app, Chrome extension, similarity search index creation script, and a frontend built with Vite and React. Users can quickly get started with DocsGPT by following the provided setup instructions and can contribute to its development by following the guidelines in the CONTRIBUTING.md file. The project follows a Code of Conduct to ensure a harassment-free community environment for all participants. DocsGPT is licensed under MIT and is built with LangChain.

Next-generation AI translation tool that provides high-quality, precise translations for various common file formats with a single click. It is based on cutting-edge large language models, offering exceptional translation quality with minimal operation, supporting multiple document formats and languages. Features include multi-format compatibility, global language translation, one-click rapid translation, flexible translation engines, and LAN sharing for efficient collaborative work.

Tunix is a JAX-based library designed for post-training Large Language Models. It provides efficient support for supervised fine-tuning, reinforcement learning, and knowledge distillation. Tunix leverages JAX for accelerated computation and integrates seamlessly with the Flax NNX modeling framework. The library is modular, efficient, and designed for distributed training on accelerators like TPUs. Currently in early development, Tunix aims to expand its capabilities, usability, and performance.

ClicShoppingAI is a powerful open-source Ecommerce solution that supports B2B, B2C, and B2B-B2C. Integrated with cutting-edge generative artificial intelligence systems like Gpt and Ollama, it helps merchants increase turnover and competitiveness for free. With AI capabilities, it optimizes inventory, offers personalized recommendations, and provides top-notch customer service. The solution is modular, lightweight, and user-friendly, with a seamless, responsive design for all devices. Installation is easy, empowering ongoing development through community support. Features include GPT API integration, generative AI functionalities, real-time safety stock predictive, WYSIWYG product description creation, image editor management, full SEO optimization, payment and shipping modules, extension system, GDPR compliance, multi-language support, and more.

PyTorch Forecasting is a PyTorch-based package for time series forecasting with state-of-the-art network architectures. It offers a high-level API for training networks on pandas data frames and utilizes PyTorch Lightning for scalable training on GPUs and CPUs. The package aims to simplify time series forecasting with neural networks by providing a flexible API for professionals and default settings for beginners. It includes a timeseries dataset class, base model class, multiple neural network architectures, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. PyTorch Forecasting is built on pytorch-lightning for easy training on various hardware configurations.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

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.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository contains recipes for building and running containerized AI and LLM applications with Podman. It provides model servers that serve machine-learning models via an API, allowing developers to quickly prototype new AI applications locally. The recipes include components like model servers and AI applications for tasks such as chat, summarization, object detection, etc. Images for sample applications and models are available in `quay.io`, and bootable containers for AI training on Linux OS are enabled.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.