Embedded inference of Deep Learning models is quite challenging due to high compute requirements. TI’s Edge AI software product helps optimize and accelerate inference on TI’s embedded devices. It supports heterogeneous execution of DNNs across cortex-A based MPUs, TI’s latest generation C7x DSP, and DNN accelerator (MMA). The solution simplifies the product life cycle of DNN development and deployment by providing a rich set of tools and optimized libraries.

SystemSculpt AI is a comprehensive AI-powered plugin for Obsidian, integrating advanced AI capabilities into note-taking, task management, knowledge organization, and content creation. It offers modules for brain integration, chat conversations, audio recording and transcription, note templates, and task generation and management. Users can customize settings, utilize AI services like OpenAI and Groq, and access documentation for detailed guidance. The plugin prioritizes data privacy by storing sensitive information locally and offering the option to use local AI models for enhanced privacy.

Learn Agentic AI is a repository that is part of the Panaversity Certified Agentic and Robotic AI Engineer program. It covers AI-201 and AI-202 courses, providing fundamentals and advanced knowledge in Agentic AI. The repository includes video playlists, projects, and project submission guidelines for students to enhance their understanding and skills in the field of AI engineering.

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.

LLM Learning Database is a comprehensive repository dedicated to AI large models, offering a curated collection of resources covering fundamental knowledge, cutting-edge technologies, and practical applications. It includes guides, case studies, code examples for model training, optimization, and deployment, as well as insightful articles from industry experts and scholars. Whether you are a beginner or an experienced learner in the field of AI large models, this repository aims to support your learning journey and foster continuous growth and progress.

AI DIAL is an open-source project that provides a platform for developing and deploying conversational AI applications. It includes components such as DIAL Core for API exposure, DIAL SDK for development, and DIAL Chat for default UI. The project offers tutorials for launching AI DIAL Chat with different models and applications, along with a user manual and configuration guide. Additionally, there are various open-source repositories related to DIAL, including DIAL Helm for helm chart, DIAL Assistant for model agnostic assistant implementation, and DIAL Analytics Realtime for usage analytics. The project aims to simplify the development and deployment of AI-powered chat applications.

The Google Cloud Document AI Samples repository contains code samples and Community Samples demonstrating how to analyze, classify, and search documents using Google Cloud Document AI. It includes various projects showcasing different functionalities such as integrating with Google Drive, processing documents using Python, content moderation with Dialogflow CX, fraud detection, language extraction, paper summarization, tax processing pipeline, and more. The repository also provides access to test document files stored in a publicly-accessible Google Cloud Storage Bucket. Additionally, there are codelabs available for optical character recognition (OCR), form parsing, specialized processors, and managing Document AI processors. Community samples, like the PDF Annotator Sample, are also included. Contributions are welcome, and users can seek help or report issues through the repository's issues page. Please note that this repository is not an officially supported Google product and is intended for demonstrative purposes only.

The OpenVINO Build and Deploy repository provides pre-built components and code samples to accelerate the development and deployment of production-grade AI applications across various industries. With the OpenVINO Toolkit from Intel, users can enhance the capabilities of both Intel and non-Intel hardware to meet specific needs. The repository includes AI reference kits, interactive demos, workshops, and step-by-step instructions for building AI applications. Additional resources such as Jupyter notebooks and a Medium blog are also available. The repository is maintained by the AI Evangelist team at Intel, who provide guidance on real-world use cases for the OpenVINO toolkit.

Cloudflare Agents is a framework for building intelligent, stateful agents that persist, think, and evolve at the edge of the network. It allows for maintaining persistent state and memory, real-time communication, processing and learning from interactions, autonomous operation at global scale, and hibernating when idle. The project is actively evolving with focus on core agent framework, WebSocket communication, HTTP endpoints, React integration, and basic AI chat capabilities. Future developments include advanced memory systems, WebRTC for audio/video, email integration, evaluation framework, enhanced observability, and self-hosting guide.

Midori AI is a cutting-edge initiative dedicated to advancing the field of artificial intelligence through research, development, and community engagement. They focus on creating innovative AI solutions, exploring novel approaches, and empowering users to harness the power of AI. Key areas of focus include cluster-based AI, AI setup assistance, AI development for Discord bots, model serving and hosting, novel AI memory architectures, and Carly - a fully simulated human with advanced AI capabilities. They have also developed the Midori AI Subsystem to streamline AI workloads by providing simplified deployment, standardized configurations, isolation for AI systems, and a growing library of backends and tools.

LLMariner is an extensible open source platform built on Kubernetes to simplify the management of generative AI workloads. It enables efficient handling of training and inference data within clusters, with OpenAI-compatible APIs for seamless integration with a wide range of AI-driven applications.

Fiction is a next-generation CMS and application framework designed to streamline the creation of AI-generated content. The first-of-its-kind platform empowers developers and content creators by integrating cutting-edge AI technologies with a robust content management system.

Coco AI is a unified search platform that connects enterprise applications and data into a single, powerful search interface. The COCO App allows users to search and interact with their enterprise data across platforms. It also offers a Gen-AI Chat for Teams tailored to team's unique knowledge and internal resources, enhancing collaboration by making information instantly accessible and providing AI-driven insights based on enterprise's specific data.

Free AI Tips is a GitHub repository that provides weekly tips on Generative AI and Machine Learning. Users can register to receive these tips for free. The repository aims to offer valuable insights and knowledge in the field of AI and ML to help individuals enhance their skills and stay updated with the latest trends and developments.

Alan AI is a comprehensive AI solution that acts as a 'unified brain' for enterprises, interconnecting applications, APIs, and data sources to streamline workflows. It offers tools like Alan AI Studio for designing dialog scenarios, lightweight SDKs for embedding AI Agents, and a backend powered by advanced AI technologies. With Alan AI, users can create conversational experiences with minimal UI changes, benefit from a serverless environment, receive on-the-fly updates, and access dialog testing and analytics tools. The platform supports various frameworks like JavaScript, React, Angular, Vue, Ember, and Electron, and provides example web apps for different platforms. Users can also explore Alan AI SDKs for iOS, Android, Flutter, Ionic, Apache Cordova, and React Native.

Lecca.io is an AI platform that enables users to configure and deploy Large Language Models (LLMs) with customizable tools and workflows. Users can easily build, customize, and automate AI agents for various tasks. The platform offers features like custom LLM configuration, tool integration, workflow builder, built-in RAG functionalities, and the ability to create custom apps and triggers. Users can also automate LLMs by setting up triggers for autonomous operation. Lecca.io provides documentation for concepts, local development, creating custom apps, adding AI providers, and running Ollama locally. Contributions are welcome, and the platform is distributed under the Apache-2.0 License with Commons Clause, with enterprise features available under a Commercial License.

Embedded inference of Deep Learning models is quite challenging due to high compute requirements. TI’s Edge AI software product helps optimize and accelerate inference on TI’s embedded devices. It supports heterogeneous execution of DNNs across cortex-A based MPUs, TI’s latest generation C7x DSP, and DNN accelerator (MMA). The solution simplifies the product life cycle of DNN development and deployment by providing a rich set of tools and optimized libraries.

The ByteIR Project is a ByteDance model compilation solution. ByteIR includes compiler, runtime, and frontends, and provides an end-to-end model compilation solution. Although all ByteIR components (compiler/runtime/frontends) are together to provide an end-to-end solution, and all under the same umbrella of this repository, each component technically can perform independently. The name, ByteIR, comes from a legacy purpose internally. The ByteIR project is NOT an IR spec definition project. Instead, in most scenarios, ByteIR directly uses several upstream MLIR dialects and Google Mhlo. Most of ByteIR compiler passes are compatible with the selected upstream MLIR dialects and Google Mhlo.

ZML is a high-performance AI inference stack built for production, using Zig language, MLIR, and Bazel. It allows users to create exciting AI projects, run pre-packaged models like MNIST, TinyLlama, OpenLLama, and Meta Llama, and compile models for accelerator runtimes. Users can also run tests, explore examples, and contribute to the project. ZML is licensed under the Apache 2.0 license.

SD.Next is an Image Diffusion implementation with advanced features. It offers multiple UI options, diffusion models, and built-in controls for text, image, batch, and video processing. The tool is multiplatform, supporting Windows, Linux, MacOS, nVidia, AMD, IntelArc/IPEX, DirectML, OpenVINO, ONNX+Olive, and ZLUDA. It provides optimized processing with the latest torch developments, including model compile, quantize, and compress functionalities. SD.Next also features Interrogate/Captioning with various models, queue management, automatic updates, and mobile compatibility.

Edgen is a local GenAI API server that serves as a drop-in replacement for OpenAI's API. It provides multi-endpoint support for chat completions and speech-to-text, is model agnostic, offers optimized inference, and features model caching. Built in Rust, Edgen is natively compiled for Windows, MacOS, and Linux, eliminating the need for Docker. It allows users to utilize GenAI locally on their devices for free and with data privacy. With features like session caching, GPU support, and support for various endpoints, Edgen offers a scalable, reliable, and cost-effective solution for running GenAI applications locally.

EasyDist is an automated parallelization system and infrastructure designed for multiple ecosystems. It offers usability by making parallelizing training or inference code effortless with just a single line of change. It ensures ecological compatibility by serving as a centralized source of truth for SPMD rules at the operator-level for various machine learning frameworks. EasyDist decouples auto-parallel algorithms from specific frameworks and IRs, allowing for the development and benchmarking of different auto-parallel algorithms in a flexible manner. The architecture includes MetaOp, MetaIR, and the ShardCombine Algorithm for SPMD sharding rules without manual annotations.

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

LLM-Viewer is a tool for visualizing Language and Learning Models (LLMs) and analyzing performance on different hardware platforms. It enables network-wise analysis, considering factors such as peak memory consumption and total inference time cost. With LLM-Viewer, users can gain valuable insights into LLM inference and performance optimization. The tool can be used in a web browser or as a command line interface (CLI) for easy configuration and visualization. The ongoing project aims to enhance features like showing tensor shapes, expanding hardware platform compatibility, and supporting more LLMs with manual model graph configuration.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.