FaceAI SDK is an on-device offline face detection, recognition, liveness detection, anti-spoofing, and 1:N/M:N face search SDK. It enables quick integration to achieve on-device face recognition, face search, and other functions. The SDK performs all functions offline on the device without the need for internet connection, ensuring privacy and security. It supports various actions for liveness detection, custom camera management, and clear imaging even in challenging lighting conditions.

Open-dLLM is the most open release of a diffusion-based large language model, providing pretraining, evaluation, inference, and checkpoints. It introduces Open-dCoder, the code-generation variant of Open-dLLM. The repo offers a complete stack for diffusion LLMs, enabling users to go from raw data to training, checkpoints, evaluation, and inference in one place. It includes pretraining pipeline with open datasets, inference scripts for easy sampling and generation, evaluation suite with various metrics, weights and checkpoints on Hugging Face, and transparent configs for full reproducibility.

HaE is a framework project in the field of network security (data security) that combines artificial intelligence (AI) large models to achieve highlighting and information extraction of HTTP messages (including WebSocket). It aims to reduce testing time, focus on valuable and meaningful messages, and improve vulnerability discovery efficiency. The project provides a clear and visual interface design, simple interface interaction, and centralized data panel for querying and extracting information. It also features built-in color upgrade algorithm, one-click export/import of data, and integration of AI large models API for optimized data processing.

Awesome AI Efficiency is a curated list of resources dedicated to enhancing efficiency in AI systems. The repository covers various topics essential for optimizing AI models and processes, aiming to make AI faster, cheaper, smaller, and greener. It includes topics like quantization, pruning, caching, distillation, factorization, compilation, parameter-efficient fine-tuning, speculative decoding, hardware optimization, training techniques, inference optimization, sustainability strategies, and scalability approaches.

Happy-LLM is a systematic learning tutorial for Large Language Models (LLM) that covers NLP research methods, LLM architecture, training process, and practical applications. It aims to help readers understand the principles and training processes of large language models. The tutorial delves into Transformer architecture, attention mechanisms, pre-training language models, building LLMs, training processes, and practical applications like RAG and Agent technologies. It is suitable for students, researchers, and LLM enthusiasts with programming experience, Python knowledge, and familiarity with deep learning and NLP concepts. The tutorial encourages hands-on practice and participation in LLM projects and competitions to deepen understanding and contribute to the open-source LLM community.

Nocode/WEP is a forward-looking office visualization platform that includes modules for document building, web application creation, presentation design, and AI capabilities for office scenarios. It supports features such as configuring bullet comments, global article comments, multimedia content, custom drawing boards, flowchart editor, form designer, keyword annotations, article statistics, custom appreciation settings, JSON import/export, content block copying, and unlimited hierarchical directories. The platform is compatible with major browsers and aims to deliver content value, iterate products, share technology, and promote open-source collaboration.

The 'END TO END GENERATIVE AI PROJECTS' repository is a collection of awesome industry projects utilizing Large Language Models (LLM) for various tasks such as chat applications with PDFs, image to speech generation, video transcribing and summarizing, resume tracking, text to SQL conversion, invoice extraction, medical chatbot, financial stock analysis, and more. The projects showcase the deployment of LLM models like Google Gemini Pro, HuggingFace Models, OpenAI GPT, and technologies such as Langchain, Streamlit, LLaMA2, LLaMAindex, and more. The repository aims to provide end-to-end solutions for different AI applications.

Agentica is a human-centric framework for building large language model agents. It provides functionalities for planning, memory management, tool usage, and supports features like reflection, planning and execution, RAG, multi-agent, multi-role, and workflow. The tool allows users to quickly code and orchestrate agents, customize prompts, and make API calls to various services. It supports API calls to OpenAI, Azure, Deepseek, Moonshot, Claude, Ollama, and Together. Agentica aims to simplify the process of building AI agents by providing a user-friendly interface and a range of functionalities for agent development.

Awesome LangGraph is a curated list of projects, resources, and tools for building stateful, multi-actor applications with LangGraph. It provides valuable resources for developers at all stages of development, from beginners to those building production-ready systems. The repository covers core ecosystem components, LangChain ecosystem, LangGraph platform, official resources, starter templates, pre-built agents, example applications, development tools, community projects, AI assistants, content & media, knowledge & retrieval, finance & business, sustainability, learning resources, companies using LangGraph, contributing guidelines, and acknowledgments.

Video-ChatGPT is a video conversation model that aims to generate meaningful conversations about videos by combining large language models with a pretrained visual encoder adapted for spatiotemporal video representation. It introduces high-quality video-instruction pairs, a quantitative evaluation framework for video conversation models, and a unique multimodal capability for video understanding and language generation. The tool is designed to excel in tasks related to video reasoning, creativity, spatial and temporal understanding, and action recognition.

nndeploy is a tool that allows you to quickly build your visual AI workflow without the need for frontend technology. It provides ready-to-use algorithm nodes for non-AI programmers, including large language models, Stable Diffusion, object detection, image segmentation, etc. The workflow can be exported as a JSON configuration file, supporting Python/C++ API for direct loading and running, deployment on cloud servers, desktops, mobile devices, edge devices, and more. The framework includes mainstream high-performance inference engines and deep optimization strategies to help you transform your workflow into enterprise-level production applications.

AI 0x0 is a versatile AI query generation desktop floating assistant application that supports MacOS and Windows. It allows users to utilize AI capabilities in any desktop software to query and generate text, images, audio, and video data, helping them work more efficiently. The application features a dynamic desktop floating ball, floating dialogue bubbles, customizable presets, conversation bookmarking, preset packages, network acceleration, query mode, input mode, mouse navigation, deep customization of ChatGPT Next Web, support for full-format libraries, online search, voice broadcasting, voice recognition, voice assistant, application plugins, multi-model support, online text and image generation, image recognition, frosted glass interface, light and dark theme adaptation for each language model, and free access to all language models except Chat0x0 with a key.

The `ipex-llm` repository is an LLM acceleration library designed for Intel GPU, NPU, and CPU. It provides seamless integration with various models and tools like llama.cpp, Ollama, HuggingFace transformers, LangChain, LlamaIndex, vLLM, Text-Generation-WebUI, DeepSpeed-AutoTP, FastChat, Axolotl, and more. The library offers optimizations for over 70 models, XPU acceleration, and support for low-bit (FP8/FP6/FP4/INT4) operations. Users can run different models on Intel GPUs, NPU, and CPUs with support for various features like finetuning, inference, serving, and benchmarking.

This repository provides a demonstration of running a native Large Language Model (LLM) on Android devices. It supports various models such as Qwen2.5-Instruct, MiniCPM-DPO/SFT, Yuan2.0, Gemma2-it, StableLM2-Chat/Zephyr, and Phi3.5-mini-instruct. The demo models are optimized for extreme execution speed after being converted from HuggingFace or ModelScope. Users can download the demo models from the provided drive link, place them in the assets folder, and follow specific instructions for decompression and model export. The repository also includes information on quantization methods and performance benchmarks for different models on various devices.

Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.

ChatMASTER is a self-built backend conversation service based on AI large model APIs, supporting synchronous and streaming responses with perfect printer effects. It supports switching between mainstream models such as DeepSeek, Kimi, Doubao, OpenAI, Claude3, Yiyan, Tongyi, Xinghuo, ChatGLM, Shusheng, and more. It also supports loading local models and knowledge bases using Ollama and Langchain, as well as online API interfaces like Coze and Gitee AI. The project includes Java server-side, web-side, mobile-side, and management background configuration. It provides various assistant types for prompt output and allows creating custom assistant templates in the management background. The project uses technologies like Spring Boot, Spring Security + JWT, Mybatis-Plus, Lombok, Mysql & Redis, with easy-to-understand code and comprehensive permission control using JWT authentication system for multi-terminal support.

AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition, Body Segmentation

This repository contains a C++ application that demonstrates face recognition capabilities using computer vision techniques. The demo utilizes OpenCV and dlib libraries for efficient face detection and recognition with 3D passive face liveness detection (face anti-spoofing). Key Features: Face detection: The SDK utilizes advanced computer vision techniques to detect faces in images or video frames, enabling a wide range of applications. Face recognition: It can recognize known faces by comparing them with a pre-defined database of individuals. Age estimation: It can estimate the age of detected faces. Gender detection: It can determine the gender of detected faces. Liveness detection: It can detect whether a face is from a live person or a static image.

FaceAPI is an AI-powered tool for face detection, rotation tracking, face description, recognition, age, gender, and emotion prediction. It can be used in both browser and NodeJS environments using TensorFlow/JS. The tool provides live demos for processing images and webcam feeds, along with NodeJS examples for various tasks such as face similarity comparison and multiprocessing. FaceAPI offers different pre-built versions for client-side browser execution and server-side NodeJS execution, with or without TFJS pre-bundled. It is compatible with TFJS 2.0+ and TFJS 3.0+.

MiKaPo is a web-based tool that allows users to pose MMD models in real-time using video input. It utilizes technologies such as Mediapipe for 3D key points detection, Babylon.js for 3D scene rendering, babylon-mmd for MMD model viewing, and Vite+React for the web framework. Users can upload videos and images, select different environments, and choose models for posing. MiKaPo also supports camera input and Ollama (electron version). The tool is open to feature requests and pull requests, with ongoing development to add VMD export functionality.

DeepSparkHub is a repository that curates hundreds of application algorithms and models covering various fields in AI and general computing. It supports mainstream intelligent computing scenarios in markets such as smart cities, digital individuals, healthcare, education, communication, energy, and more. The repository provides a wide range of models for tasks such as computer vision, face detection, face recognition, instance segmentation, image generation, knowledge distillation, network pruning, object detection, 3D object detection, OCR, pose estimation, self-supervised learning, semantic segmentation, super resolution, tracking, traffic forecast, GNN, HPC, methodology, multimodal, NLP, recommendation, reinforcement learning, speech recognition, speech synthesis, and 3D reconstruction.

FaceAiSharp is a .NET library designed for face-related computer vision tasks. It offers functionalities such as face detection, face recognition, facial landmarks detection, and eye state detection. The library utilizes pretrained ONNX models for accurate and efficient results, enabling users to integrate these capabilities into their .NET applications easily. With a focus on simplicity and performance, FaceAiSharp provides a local processing solution without relying on cloud services, supporting image-based face processing using ImageSharp. It is cross-platform compatible, supporting Windows, Linux, Android, and more.

Symposium2023 is a project aimed at enabling Delphi users to incorporate AI technology into their applications. It provides generalized interfaces to different AI models, making them easily accessible. The project showcases AI's versatility in tasks like language translation, human-like conversations, image generation, data analysis, and more. Users can experiment with different AI models, change providers easily, and avoid vendor lock-in. The project supports various AI features like vision support and function calling, utilizing providers like Google, Microsoft Azure, Amazon, OpenAI, and more. It includes example programs demonstrating tasks such as text-to-speech, language translation, face detection, weather querying, audio transcription, voice recognition, image generation, invoice processing, and API testing. The project also hints at potential future research areas like using embeddings for data search and integrating Python AI libraries with Delphi.

FaceAI SDK is an on-device offline face detection, recognition, liveness detection, anti-spoofing, and 1:N/M:N face search SDK. It enables quick integration to achieve on-device face recognition, face search, and other functions. The SDK performs all functions offline on the device without the need for internet connection, ensuring privacy and security. It supports various actions for liveness detection, custom camera management, and clear imaging even in challenging lighting conditions.

FaceAI SDK is an on-device offline face detection, recognition, liveness detection, anti-spoofing, and 1:N/M:N face search SDK. It enables quick integration to achieve on-device face recognition, face search, and other functions. The SDK performs all functions offline on the device without the need for internet connection, ensuring privacy and security. It supports various actions for liveness detection, custom camera management, and clear imaging even in challenging lighting conditions.

Protofy is a full-stack, batteries-included low-code enabled web/app and IoT system with an API system and real-time messaging. It is based on Protofy (protoflow + visualui + protolib + protodevices) + Expo + Next.js + Tamagui + Solito + Express + Aedes + Redbird + Many other amazing packages. Protofy can be used to fast prototype Apps, webs, IoT systems, automations, or APIs. It is a ultra-extensible CMS with supercharged capabilities, mobile support, and IoT support (esp32 thanks to esphome).

The Google Generative AI SDK for Dart enables developers to utilize cutting-edge Large Language Models (LLMs) for creating language applications. It provides access to the Gemini API for generating content using state-of-the-art models. Developers can integrate the SDK into their Dart or Flutter applications to leverage powerful AI capabilities. It is recommended to use the SDK for server-side API calls to ensure the security of API keys and protect against potential key exposure in mobile or web apps.

visionOS-examples is a repository containing accelerators for Spatial Computing. It includes examples such as Local Large Language Model, Chat Apple Vision Pro, WebSockets, Anchor To Head, Hand Tracking, Battery Life, Countdown, Plane Detection, Timer Vision, and PencilKit for visionOS. The repository showcases various functionalities and features for Apple Vision Pro, offering tools for developers to enhance their visionOS apps with capabilities like hand tracking, plane detection, and real-time cryptocurrency prices.

Gemini Pro Vision Playground is a simple project aimed at assisting developers in utilizing the Gemini Pro Vision and Gemini Pro AI models for building applications. It provides a playground environment for experimenting with these models and integrating them into apps. The project includes instructions for setting up the Google AI API key and running the development server to visualize the results. Developers can learn more about the Gemini API documentation and Next.js framework through the provided resources. The project encourages contributions and feedback from the community.

TikTok Automation Bot is an Appium-based tool for automating TikTok account creation and video posting on real devices. It offers functionalities such as automated account creation and video posting, along with integrations like Crane tweak, SMSActivate service, and IPQualityScore service. The tool also provides device and automation management system, anti-bot system for human behavior modeling, and IP rotation system for different IP addresses. It is designed to simplify the process of managing TikTok accounts and posting videos efficiently.

General is a DART & Flutter library created by AZKADEV to speed up development on various platforms and CLI easily. It allows access to features such as camera, fingerprint, SMS, and MMS. The library is designed for Dart language and provides functionalities for app background, text to speech, speech to text, and more.

Shards is a high-performance, multi-platform, type-safe programming language designed for visual development. It is a dataflow visual programming language that enables building full-fledged apps and games without traditional coding. Shards features automatic type checking, optimized shard implementations for high performance, and an intuitive visual workflow for beginners. The language allows seamless round-trip engineering between code and visual models, empowering users to create multi-platform apps easily. Shards also powers an upcoming AI-powered game creation system, enabling real-time collaboration and game development in a low to no-code environment.