The 'mslearn-ai-vision' repository contains lab files for Azure AI Vision modules. It provides hands-on exercises and resources for learning about AI vision capabilities on the Azure platform. The labs cover topics such as image recognition, object detection, and image classification using Azure's AI services. By following the lab exercises, users can gain practical experience in building and deploying AI vision solutions in the cloud.

This repository contains lab files for Azure AI Language modules. It provides hands-on exercises and resources for learning about various AI language technologies on the Azure platform. The labs cover topics such as natural language processing, text analytics, language understanding, and more. By following the exercises in this repository, users can gain practical experience in implementing AI language solutions using Azure services.

The mslearn-knowledge-mining repository contains lab files for Azure AI Knowledge Mining modules. It provides resources for learning and implementing knowledge mining techniques using Azure AI services. The repository is designed to help users explore and understand how to leverage AI for knowledge mining purposes within the Azure ecosystem.

The Azure Open AI Application Landing Zone Solution Accelerator aims to assist in setting up development and production environments for Generative AI solutions using Azure Open AI and Azure Services. It provides deployment templates for common Gen AI solution patterns and offers customization options. The solution accelerator also offers best practices for technology usage in various scenarios.

Repository for training and deploying Generative AI models, including text-text, text-to-image generation, prompt engineering playground and chain of thought examples using SageMaker Studio. The tool provides a platform for users to experiment with generative AI techniques, enabling them to create text and image outputs based on input data. It offers a range of functionalities for training and deploying models, as well as exploring different generative AI applications.

The 'ai-demos' repository is a collection of example code from presentations focusing on building with AI and LLMs. It serves as a resource for developers looking to explore practical applications of artificial intelligence in their projects. The code snippets showcase various techniques and approaches to leverage AI technologies effectively. The repository aims to inspire and educate developers on integrating AI solutions into their applications.

This repository is part of the Certified Cloud Native Applied Generative AI Engineer program, focusing on Applied Generative AI Fundamentals. It covers prompt engineering, developing custom GPTs, and Multi AI Agent Systems. The course helps in building a strong understanding of generative AI, applying Large Language Models (LLMs) and diffusion models practically. It introduces principles of prompt engineering to work efficiently with AI, creating custom AI models and GPTs using OpenAI, Azure, and Google technologies. It also utilizes open source libraries like LangChain, CrewAI, and LangGraph to automate tasks and business processes.

AI Toolkit is a collection of community created scripts that leverage AI to improve your life. It includes tools like AI CLI for generating terminal commands from natural language queries, AI Commit for generating commit messages using LLMs, and Research Assistant for researching and summarizing information from multiple sources.

Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.

The 'ai_agents_cookbooks' repository contains cookbooks for AI agents, which are AI systems capable of using other software as tools. It provides resources for learning more about AI through events and requires Python 3.10 or higher as a prerequisite.

The Google Cloud Applied AI Engineering repository provides reference guides, blueprints, code samples, and hands-on labs developed by the Google Cloud Applied AI Engineering team. It contains resources for Generative AI on Vertex AI, including code samples and hands-on labs demonstrating the use of Generative AI models and tools in Vertex AI. Additionally, it offers reference guides and blueprints that compile best practices and prescriptive guidance for running large-scale AI/ML workloads on Google Cloud AI/ML infrastructure.

This repository contains a collection of AI projects implemented in Python, primarily in Jupyter notebooks. The projects cover various aspects of artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, and more. Each project is designed to showcase different AI techniques and algorithms, providing a hands-on learning experience for users interested in exploring the field of artificial intelligence.

OmniChain is a tool for building efficient self-updating visual workflows using AI language models, enabling users to automate tasks, create chatbots, agents, and integrate with existing frameworks. It allows users to create custom workflows guided by logic processes, store and recall information, and make decisions based on that information. The tool enables users to create tireless robot employees that operate 24/7, access the underlying operating system, generate and run NodeJS code snippets, and create custom agents and logic chains. OmniChain is self-hosted, open-source, and available for commercial use under the MIT license, with no coding skills required.

FedML is a unified and scalable machine learning library for running training and deployment anywhere at any scale. It is highly integrated with FEDML Nexus AI, a next-gen cloud service for LLMs & Generative AI. FEDML Nexus AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds.

Generative-AI-Drug-Discovery is a public repository on GitHub focused on using tensor network machine learning approaches to accelerate GenAI for drug discovery. The repository aims to implement effective architectures and methodologies into Large Language Models (LLMs) to enhance Drug Discovery Generative AI performance.

This repository supports the YOLO series models and provides an AI auto-aiming tool based on YOLO-TensorRT for the game CrossFire. Users can refer to the provided link for compilation and running instructions. The tool includes functionalities for screenshot + inference, mouse movement, and smooth mouse movement. The next goal is to automatically set the optimal PID parameters on the local machine. Developers are welcome to contribute to the improvement of this tool.

Hyper-Pretrained Transformers (HPT) is a novel multimodal LLM framework from HyperGAI, trained for vision-language models capable of understanding both textual and visual inputs. The repository contains the open-source implementation of inference code to reproduce the evaluation results of HPT Air on different benchmarks. HPT has achieved competitive results with state-of-the-art models on various multimodal LLM benchmarks. It offers models like HPT 1.5 Air and HPT 1.0 Air, providing efficient solutions for vision-and-language tasks.

LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.

Spark AI Free 服务 provides high-speed streaming output, multi-turn dialogue support, AI drawing support, long document interpretation, and image parsing. It offers zero-configuration deployment, multi-token support, and automatic session trace cleaning. It is fully compatible with the ChatGPT interface. The repository includes multiple free-api projects for various AI services. Users can access the API for tasks such as chat completions, AI drawing, document interpretation, image analysis, and ssoSessionId live checking. The project also provides guidelines for deployment using Docker, Docker-compose, Render, Vercel, and native deployment methods. It recommends using custom clients for faster and simpler access to the free-api series projects.

MLX-VLM is a package designed for running Vision LLMs on Mac systems using MLX. It provides a convenient way to install and utilize the package for processing large language models related to vision tasks. The tool simplifies the process of running LLMs on Mac computers, offering a seamless experience for users interested in leveraging MLX for vision-related projects.

This is the official Clarifai gRPC Python client for interacting with their recognition API. Clarifai offers a platform for data scientists, developers, researchers, and enterprises to utilize artificial intelligence for image, video, and text analysis through computer vision and natural language processing. The client allows users to authenticate, predict concepts in images, and access various functionalities provided by the Clarifai API. It follows a versioning scheme that aligns with the backend API updates and includes specific instructions for installation and troubleshooting. Users can explore the Clarifai demo, sign up for an account, and refer to the documentation for detailed information.

This repository provides the latest implementation for the AI Horde Worker, allowing users to utilize their graphics card(s) to generate, post-process, or analyze images for others. It offers a platform where users can create images and earn 'kudos' in return, granting priority for their own image generations. The repository includes important details for setup, recommendations for system configurations, instructions for installation on Windows and Linux, basic usage guidelines, and information on updating the AI Horde Worker. Users can also run the worker with multiple GPUs and receive notifications for updates through Discord. Additionally, the repository contains models that are licensed under the CreativeML OpenRAIL License.

Geospy is a Python tool that utilizes Graylark's AI-powered geolocation service to determine the location where photos were taken. It allows users to analyze images and retrieve information such as country, city, explanation, coordinates, and Google Maps links. The tool provides a seamless way to integrate geolocation services into various projects and applications.

Awesome-Colorful-LLM is a meticulously assembled anthology of vibrant multimodal research focusing on advancements propelled by large language models (LLMs) in domains such as Vision, Audio, Agent, Robotics, and Fundamental Sciences like Mathematics. The repository contains curated collections of works, datasets, benchmarks, projects, and tools related to LLMs and multimodal learning. It serves as a comprehensive resource for researchers and practitioners interested in exploring the intersection of language models and various modalities for tasks like image understanding, video pretraining, 3D modeling, document understanding, audio analysis, agent learning, robotic applications, and mathematical research.

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.