OpenAI-Forward is an efficient forwarding service implemented for large language models. Its core features include user request rate control, token rate limiting, intelligent prediction caching, log management, and API key management, aiming to provide efficient and convenient model forwarding services. Whether proxying local language models or cloud-based language models like LocalAI or OpenAI, OpenAI-Forward makes it easy. Thanks to support from libraries like uvicorn, aiohttp, and asyncio, OpenAI-Forward achieves excellent asynchronous performance.

Streamer-Sales is a large model for live streamers that can explain products based on their characteristics and inspire users to make purchases. It is designed to enhance sales efficiency and user experience, whether for online live sales or offline store promotions. The model can deeply understand product features and create tailored explanations in vivid and precise language, sparking user's desire to purchase. It aims to revolutionize the shopping experience by providing detailed and unique product descriptions to engage users effectively.

This project is a smart chatbot based on a large model, supporting WeChat, WeChat Official Account, Feishu, and DingTalk access. You can choose from GPT3.5/GPT4.0/Claude/Wenxin Yanyi/Xunfei Xinghuo/Tongyi Qianwen/Gemini/LinkAI/ZhipuAI, which can process text, voice, and images, and access external resources such as operating systems and the Internet through plugins, supporting the development of enterprise AI applications based on proprietary knowledge bases.

This project is a personal implementation and reproduction of a small-parameter Chinese LLM. It mainly refers to these two open source projects: https://github.com/charent/Phi2-mini-Chinese and https://github.com/DLLXW/baby-llama2-chinese. It includes the complete process of pre-training, SFT instruction fine-tuning, DPO, and PPO (to be done). I hope to share it with everyone and hope that everyone can work together to improve it!

GitHub Sentinel is an intelligent information retrieval and high-value content mining AI Agent designed for the era of large models (LLMs). It is aimed at users who need frequent and large-scale information retrieval, especially open source enthusiasts, individual developers, and investors. The main features include subscription management, update retrieval, notification system, report generation, multi-model support, scheduled tasks, graphical interface, containerization, continuous integration, and the ability to track and analyze the latest dynamics of GitHub open source projects and expand to other information channels like Hacker News for comprehensive information mining and analysis capabilities.

HuggingLLM is a project that aims to introduce ChatGPT to a wider audience, particularly those interested in using the technology to create new products or applications. The project focuses on providing practical guidance on how to use ChatGPT-related APIs to create new features and applications. It also includes detailed background information and system design introductions for relevant tasks, as well as example code and implementation processes. The project is designed for individuals with some programming experience who are interested in using ChatGPT for practical applications, and it encourages users to experiment and create their own applications and demos.

The 'meet-libai' project aims to promote and popularize the cultural heritage of the Chinese poet Li Bai by constructing a knowledge graph of Li Bai and training a professional AI intelligent body using large models. The project includes features such as data preprocessing, knowledge graph construction, question-answering system development, and visualization exploration of the graph structure. It also provides code implementations for large models and RAG retrieval enhancement.

LangChain4j-AIDeepin is an open-source, offline deployable retrieval enhancement generation (RAG) project based on large language models such as ChatGPT and Langchain4j application framework. It offers features like registration & login, multi-session support, image generation, prompt words, quota control, knowledge base, model-based search, model switching, and search engine switching. The project integrates models like ChatGPT 3.5, Tongyi Qianwen, Wenxin Yiyuan, Ollama, and DALL-E 2. The backend uses technologies like JDK 17, Spring Boot 3.0.5, Langchain4j, and PostgreSQL with pgvector extension, while the frontend is built with Vue3, TypeScript, and PNPM.

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.

VoAPI is a new high-value/high-performance AI model interface management and distribution system. It is a closed-source tool for personal learning use only, not for commercial purposes. Users must comply with upstream AI model service providers and legal regulations. The system offers a visually appealing interface, independent development documentation page support, service monitoring page configuration support, and third-party login support. It also optimizes interface elements, user registration time support, data operation button positioning, and more.

EduChat is a large-scale language model-based chatbot system designed for intelligent education by the EduNLP team at East China Normal University. The project focuses on developing a dialogue-based language model for the education vertical domain, integrating diverse education vertical domain data, and providing functions such as automatic question generation, homework correction, emotional support, course guidance, and college entrance examination consultation. The tool aims to serve teachers, students, and parents to achieve personalized, fair, and warm intelligent education.

lassxToolkit is a versatile tool designed for file processing tasks. It allows users to manipulate files and folders based on specified configurations in a strict .json format. The tool supports various AI models for tasks such as image upscaling and denoising. Users can customize settings like input/output paths, error handling, file selection, and plugin integration. lassxToolkit provides detailed instructions on configuration options, default values, and model selection. It also offers features like tree restoration, recursive processing, and regex-based file filtering. The tool is suitable for users looking to automate file processing tasks with AI capabilities.

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.

VoAPI is a new high-value/high-performance AI model interface management and distribution system. It is a closed-source tool for personal learning use only, not for commercial purposes. Users must comply with upstream AI model service providers and legal regulations. The system offers a visually appealing interface with features such as independent development documentation page support, service monitoring page configuration support, and third-party login support. Users can manage user registration time, optimize interface elements, and support features like online recharge, model pricing display, and sensitive word filtering. VoAPI also provides support for various AI models and platforms, with the ability to configure homepage templates, model information, and manufacturer information.

Metaso AI Free service supports high-speed streaming output, secret tower AI super network search (full network or academic as well as concise, in-depth, research three modes), zero-configuration deployment, multi-token support. Fully compatible with ChatGPT interface. It also has seven other free APIs available for use. The tool provides various deployment options such as Docker, Docker-compose, Render, Vercel, and native deployment. Users can access the tool for chat completions and token live checks. Note: Reverse API is unstable, it is recommended to use the official Metaso AI website to avoid the risk of banning. This project is for research and learning purposes only, not for commercial use.

DeepSeek Free API is a high-speed streaming output tool that supports multi-turn conversations and zero-configuration deployment. It is compatible with the ChatGPT interface and offers multiple token support. The tool provides eight free APIs for various AI interfaces. Users can access the tool online, prepare for integration, deploy using Docker, Docker-compose, Render, Vercel, or native deployment methods. It also offers client recommendations for faster integration and supports dialogue completion and userToken live checks. The tool comes with important considerations for Nginx reverse proxy optimization and token statistics.

OpenAI-Forward is an efficient forwarding service implemented for large language models. Its core features include user request rate control, token rate limiting, intelligent prediction caching, log management, and API key management, aiming to provide efficient and convenient model forwarding services. Whether proxying local language models or cloud-based language models like LocalAI or OpenAI, OpenAI-Forward makes it easy. Thanks to support from libraries like uvicorn, aiohttp, and asyncio, OpenAI-Forward achieves excellent asynchronous performance.

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.

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.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.