AMchat is a large language model that integrates advanced math concepts, exercises, and solutions. The model is based on the InternLM2-Math-7B model and is specifically designed to answer advanced math problems. It provides a comprehensive dataset that combines Math and advanced math exercises and solutions. Users can download the model from ModelScope or OpenXLab, deploy it locally or using Docker, and even retrain it using XTuner for fine-tuning. The tool also supports LMDeploy for quantization, OpenCompass for evaluation, and various other features for model deployment and evaluation. The project contributors have provided detailed documentation and guides for users to utilize the tool effectively.

CareGPT is a medical large language model (LLM) that explores medical data, training, and deployment related research work. It integrates resources, open-source models, rich data, and efficient deployment methods. It supports various medical tasks, including patient diagnosis, medical dialogue, and medical knowledge integration. The model has been fine-tuned on diverse medical datasets to enhance its performance in the healthcare domain.

The langchain4j-aideepin-web repository is the frontend project of langchain4j-aideepin, an open-source, offline deployable retrieval enhancement generation (RAG) project based on large language models such as ChatGPT and application frameworks such as Langchain4j. It includes features like registration & login, multi-sessions (multi-roles), image generation (text-to-image, image editing, image-to-image), suggestions, quota control, knowledge base (RAG) based on large models, model switching, and search engine switching.

New API is an open-source project based on One API with additional features and improvements. It offers a new UI interface, supports Midjourney-Proxy(Plus) interface, online recharge functionality, model-based charging, channel weight randomization, data dashboard, token-controlled models, Telegram authorization login, Suno API support, Rerank model integration, and various third-party models. Users can customize models, retry channels, and configure caching settings. The deployment can be done using Docker with SQLite or MySQL databases. The project provides documentation for Midjourney and Suno interfaces, and it is suitable for AI enthusiasts and developers looking to enhance AI capabilities.

WeChat Bot is a simple and easy-to-use WeChat robot based on chatgpt and wechaty. It can help you automatically reply to WeChat messages or manage WeChat groups/friends. The tool requires configuration of AI services such as Xunfei, Kimi, or ChatGPT. Users can customize the tool to automatically reply to group or private chat messages based on predefined conditions. The tool supports running in Docker for easy deployment and provides a convenient way to interact with various AI services for WeChat automation.

Thor is a powerful AI model management tool designed for unified management and usage of various AI models. It offers features such as user, channel, and token management, data statistics preview, log viewing, system settings, external chat link integration, and Alipay account balance purchase. Thor supports multiple AI models including OpenAI, Kimi, Starfire, Claudia, Zhilu AI, Ollama, Tongyi Qianwen, AzureOpenAI, and Tencent Hybrid models. It also supports various databases like SqlServer, PostgreSql, Sqlite, and MySql, allowing users to choose the appropriate database based on their needs.

Airdrop-tools is a repository containing tools for all Telegram bots. Users can join the Telegram group for support and access various bot apps like Moonbix, Blum, Major, Memefi, and more. The setup requires Node.js and Python, with instructions on creating data directories and installing extensions. Users can run different tools like Blum, Major, Moonbix, Yescoin, Matchain, Fintopio, Agent301, IAMDOG, Banana, Cats, Wonton, and Xkucoin by following specific commands. The repository also provides contact information and options for supporting the creator.

ChatGPT Web is a web application that provides access to the ChatGPT API. It offers two non-official methods to interact with ChatGPT: through the ChatGPTAPI (using the `gpt-3.5-turbo-0301` model) or through the ChatGPTUnofficialProxyAPI (using a web access token). The ChatGPTAPI method is more reliable but requires an OpenAI API key, while the ChatGPTUnofficialProxyAPI method is free but less reliable. The application includes features such as user registration and login, synchronization of conversation history, customization of API keys and sensitive words, and management of users and keys. It also provides a user interface for interacting with ChatGPT and supports multiple languages and themes.

Gzm Design is a free and open-source poster designer developed using the latest mainstream technologies such as Vue3, Vite4, TypeScript, etc. It provides features like PSD import, JSON import, multiple pages support, shortcut key support, template import, layer management, ruler tool, pen tool, element editing, preview, file download, canvas zooming and dragging, border stroke, filling, blending modes, text formatting, group handling, canvas size modification, rich text support, masking, shadow effects, undo/redo functionality, QR code tool, barcode tool, and ruler line npm package encapsulation.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It allows users to communicate with the Ollama server and manage models for various deployment scenarios. The library provides APIs for interacting with Ollama, generating fake data, testing UI interactions, translating messages, and building web UIs. Users can easily integrate Ollama4j into their Java projects to leverage the functionalities offered by the Ollama server.

RTXZY-MD is a bot tool that supports file hosting, QR code, pairing code, and RestApi features. Users must fill in the Apikey for the bot to function properly. It is not recommended to install the bot on platforms lacking ffmpeg, imagemagick, webp, or express.js support. The tool allows for 95% implementation of website api and supports free and premium ApiKeys. Users can join group bots and get support from Sociabuzz. The tool can be run on Heroku with specific buildpacks and is suitable for Windows/VPS/RDP users who need Git, NodeJS, FFmpeg, and ImageMagick installations.

ChatPilot is a chat agent tool that enables AgentChat conversations, supports Google search, URL conversation (RAG), and code interpreter functionality, replicates Kimi Chat (file, drag and drop; URL, send out), and supports OpenAI/Azure API. It is based on LangChain and implements ReAct and OpenAI Function Call for agent Q&A dialogue. The tool supports various automatic tools such as online search using Google Search API, URL parsing tool, Python code interpreter, and enhanced RAG file Q&A with query rewriting support. It also allows front-end and back-end service separation using Svelte and FastAPI, respectively. Additionally, it supports voice input/output, image generation, user management, permission control, and chat record import/export.

One API 是一个开源项目，它通过标准的 OpenAI API 格式访问所有的大模型，开箱即用。它支持多种大模型，包括 OpenAI ChatGPT 系列模型、Anthropic Claude 系列模型、Google PaLM2/Gemini 系列模型、Mistral 系列模型、百度文心一言系列模型、阿里通义千问系列模型、讯飞星火认知大模型、智谱 ChatGLM 系列模型、360 智脑、腾讯混元大模型、Moonshot AI、百川大模型、MINIMAX、Groq、Ollama、零一万物、阶跃星辰。One API 还支持配置镜像以及众多第三方代理服务，支持通过负载均衡的方式访问多个渠道，支持 stream 模式，支持多机部署，支持令牌管理，支持兑换码管理，支持渠道管理，支持用户分组以及渠道分组，支持渠道设置模型列表，支持查看额度明细，支持用户邀请奖励，支持以美元为单位显示额度，支持发布公告，设置充值链接，设置新用户初始额度，支持模型映射，支持失败自动重试，支持绘图接口，支持 Cloudflare AI Gateway，支持丰富的自定义设置，支持通过系统访问令牌调用管理 API，进而**在无需二开的情况下扩展和自定义** One API 的功能，支持 Cloudflare Turnstile 用户校验，支持用户管理，支持多种用户登录注册方式，支持主题切换，配合 Message Pusher 可将报警信息推送到多种 App 上。

Tianji is a free, non-commercial artificial intelligence system developed by SocialAI for tasks involving worldly wisdom, such as etiquette, hospitality, gifting, wishes, communication, awkwardness resolution, and conflict handling. It includes four main technical routes: pure prompt, Agent architecture, knowledge base, and model training. Users can find corresponding source code for these routes in the tianji directory to replicate their own vertical domain AI applications. The project aims to accelerate the penetration of AI into various fields and enhance AI's core competencies.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.

EasyNovelAssistant is a simple novel generation assistant powered by a lightweight and uncensored Japanese local LLM 'LightChatAssistant-TypeB'. It allows for perpetual generation with 'Generate forever' feature, stacking up lucky gacha draws. It also supports text-to-speech. Users can directly utilize KoboldCpp and Style-Bert-VITS2 internally or use EasySdxlWebUi to generate images while using the tool. The tool is designed for local novel generation with a focus on ease of use and flexibility.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.