MaiBot is an intelligent QQ group chat bot based on a large language model. It is developed using the nonebot2 framework, with LLM providing conversation abilities, MongoDB for data persistence support, and NapCat as the QQ protocol endpoint support. The project is in active development stage, with features like chat functionality, emoji functionality, schedule management, memory function, knowledge base function, and relationship function planned for future updates. The project aims to create a 'life form' active in QQ group chats, focusing on companionship and creating a more human-like presence rather than a perfect assistant. The application generates content from AI models, so users are advised to discern carefully and not use it for illegal purposes.

This repository contains a chatbot tool powered by AI technology. The chatbot is designed to interact with users in a conversational manner, providing information and assistance on various topics. Users can engage with the chatbot to ask questions, seek recommendations, or simply have a casual conversation. The AI technology behind the chatbot enables it to understand natural language inputs and provide relevant responses, making the interaction more intuitive and engaging. The tool is versatile and can be customized for different use cases, such as customer support, information retrieval, or entertainment purposes. Overall, the chatbot offers a user-friendly and interactive experience, leveraging AI to enhance communication and engagement.

Chatmcp is a chatbot framework for building conversational AI applications. It provides a flexible and extensible platform for creating chatbots that can interact with users in a natural language. With Chatmcp, developers can easily integrate chatbot functionality into their applications, enabling users to communicate with the system through text-based conversations. The framework supports various natural language processing techniques and allows for the customization of chatbot behavior and responses. Chatmcp simplifies the development of chatbots by providing a set of pre-built components and tools that streamline the creation process. Whether you are building a customer support chatbot, a virtual assistant, or a chat-based game, Chatmcp offers the necessary features and capabilities to bring your conversational AI ideas to life.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

This repository contains a collection of AI algorithms and models for various machine learning tasks. It provides implementations of popular algorithms such as neural networks, decision trees, and support vector machines. The code is well-documented and easy to understand, making it suitable for both beginners and experienced developers. The repository also includes example datasets and tutorials to help users get started with building and training AI models. Whether you are a student learning about AI or a professional working on machine learning projects, this repository can be a valuable resource for your development journey.

SciML.ai is an open source software organization dedicated to unifying packages for scientific machine learning. It focuses on developing modular scientific simulation support software, including differential equation solvers, inverse problems methodologies, and automated model discovery. The organization aims to provide a diverse set of tools with a common interface, creating a modular, easily-extendable, and highly performant ecosystem for scientific simulations. The website serves as a platform to showcase SciML organization's packages and share news within the ecosystem. Pull requests are encouraged for contributions.

Deepteam is a powerful open-source tool designed for deep learning projects. It provides a user-friendly interface for training, testing, and deploying deep neural networks. With Deepteam, users can easily create and manage complex models, visualize training progress, and optimize hyperparameters. The tool supports various deep learning frameworks and allows seamless integration with popular libraries like TensorFlow and PyTorch. Whether you are a beginner or an experienced deep learning practitioner, Deepteam simplifies the development process and accelerates model deployment.

OllamaSharp is a .NET binding for the Ollama API, providing an intuitive API client to interact with Ollama. It offers support for all Ollama API endpoints, real-time streaming, progress reporting, and an API console for remote management. Users can easily set up the client, list models, pull models with progress feedback, stream completions, and build interactive chats. The project includes a demo console for exploring and managing the Ollama host.

Parlant is a structured approach to building and guiding customer-facing AI agents. It allows developers to create and manage robust AI agents, providing specific feedback on agent behavior and helping understand user intentions better. With features like guidelines, glossary, coherence checks, dynamic context, and guided tool use, Parlant offers control over agent responses and behavior. Developer-friendly aspects include instant changes, Git integration, clean architecture, and type safety. It enables confident deployment with scalability, effective debugging, and validation before deployment. Parlant works with major LLM providers and offers client SDKs for Python and TypeScript. The tool facilitates natural customer interactions through asynchronous communication and provides a chat UI for testing new behaviors before deployment.

The mcp-fundamentals repository is a collection of fundamental concepts and examples related to microservices, cloud computing, and DevOps. It covers topics such as containerization, orchestration, CI/CD pipelines, and infrastructure as code. The repository provides hands-on exercises and code samples to help users understand and apply these concepts in real-world scenarios. Whether you are a beginner looking to learn the basics or an experienced professional seeking to refresh your knowledge, mcp-fundamentals has something for everyone.

BentoVLLM is an example project demonstrating how to serve and deploy open-source Large Language Models using vLLM, a high-throughput and memory-efficient inference engine. It provides a basis for advanced code customization, such as custom models, inference logic, or vLLM options. The project allows for simple LLM hosting with OpenAI compatible endpoints without the need to write any code. Users can interact with the server using Swagger UI or other methods, and the service can be deployed to BentoCloud for better management and scalability. Additionally, the repository includes integration examples for different LLM models and tools.

LightLLM is a lightweight library for linear and logistic regression models. It provides a simple and efficient way to train and deploy machine learning models for regression tasks. The library is designed to be easy to use and integrate into existing projects, making it suitable for both beginners and experienced data scientists. With LightLLM, users can quickly build and evaluate regression models using a variety of algorithms and hyperparameters. The library also supports feature engineering and model interpretation, allowing users to gain insights from their data and make informed decisions based on the model predictions.

Langfuse Docs is a repository for langfuse.com, built on Nextra. It provides guidelines for contributing to the documentation using GitHub Codespaces and local development setup. The repository includes Python cookbooks in Jupyter notebooks format, which are converted to markdown for rendering on the site. It also covers media management for images, videos, and gifs. The stack includes Nextra, Next.js, shadcn/ui, and Tailwind CSS. Additionally, there is a bundle analysis feature to analyze the production build bundle size using @next/bundle-analyzer.

DAILA is a unified interface for AI systems in decompilers, supporting various decompilers and AI systems. It allows users to utilize local and remote LLMs, like ChatGPT and Claude, and local models such as VarBERT. DAILA can be used as a decompiler plugin with GUI or as a scripting library. It also provides a Docker container for offline installations and supports tasks like summarizing functions and renaming variables in decompilation.

Trae-agent is a Python library for building and training reinforcement learning agents. It provides a simple and flexible framework for implementing various reinforcement learning algorithms and experimenting with different environments. With Trae-agent, users can easily create custom agents, define reward functions, and train them on a variety of tasks. The library also includes utilities for visualizing agent performance and analyzing training results, making it a valuable tool for both beginners and experienced researchers in the field of reinforcement learning.

OpenCopilot is a tool that allows users to create their own AI copilot for their products. It integrates with APIs to execute calls as needed, using LLMs to determine the appropriate endpoint and payload. Users can define API actions, validate schemas, and integrate a user-friendly chat bubble into their SaaS app. The tool is capable of calling APIs, transforming responses, and populating request fields based on context. It is not suitable for handling large APIs without JSON transformers. Users can teach the copilot via flows and embed it in their app with minimal code.

MaxKB is a knowledge base Q&A system based on the LLM large language model. MaxKB = Max Knowledge Base, which aims to become the most powerful brain of the enterprise.

Large Language Models (LLM) are used to browse the Wolfram directory and associated URLs to create the category structure and good word embeddings. The goal is to generate enriched prompts for GPT, Wikipedia, Arxiv, Google Scholar, Stack Exchange, or Google search. The focus is on one subdirectory: Probability & Statistics. Documentation is in the project textbook `Projects4.pdf`, which is available in the folder. It is recommended to download the document and browse your local copy with Chrome, Edge, or other viewers. Unlike on GitHub, you will be able to click on all the links and follow the internal navigation features. Look for projects related to NLP and LLM / xLLM. The best starting point is project 7.2.2, which is the core project on this topic, with references to all satellite projects. The project textbook (with solutions to all projects) is the core document needed to participate in the free course (deep tech dive) called **GenAI Fellowship**. For details about the fellowship, follow the link provided. An uncompressed version of `crawl_final_stats.txt.gz` is available on Google drive, which contains all the crawled data needed as input to the Python scripts in the XLLM5 and XLLM6 folders.

BlossomLM is a series of open-source conversational large language models. This project aims to provide a high-quality general-purpose SFT dataset in both Chinese and English, making fine-tuning accessible while also providing pre-trained model weights. **Hint**: BlossomLM is a personal non-commercial project.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

This repository contains a WhatsApp bot that utilizes OpenAI's GPT and DALL-E 2 to respond to user inputs. Users can interact with the bot through voice messages, which are transcribed and responded to. The bot requires Node.js, npm, an OpenAI API key, and a WhatsApp account. It uses Puppeteer to run a real instance of Whatsapp Web to avoid being blocked. However, there is a risk of being blocked by WhatsApp as it does not allow bots or unofficial clients on its platform. The bot is not free to use, and users will be charged by OpenAI for each request made.

Discord AI Bot is a chatbot designed to interact with Ollama and AUTOMATIC1111 Stable Diffusion on Discord. The project is now archived due to lack of maintenance. Users can set up the bot by installing Node.js, Ollama, and a model, creating a Discord bot, and starting the bot with the necessary configurations. Additionally, Docker setup instructions are provided for easy deployment. The bot can be interacted with by mentioning it in Discord messages.

J.A.R.V.I.S. is an offline large language model fine-tuned on custom and open datasets to mimic Jarvis's dialog with Stark. It prioritizes privacy by running locally and excels in responding like Jarvis with a similar tone. Current features include time/date queries, web searches, playing YouTube videos, and webcam image descriptions. Users can interact with Jarvis via command line after installing the model locally using Ollama. Future plans involve voice cloning, voice-to-text input, and deploying the voice model as an API.

AirDC++ Web Client is a locally installed application designed for flexible file sharing within groups over a local network or the internet. It utilizes the Advanced Direct Connect protocol to create file sharing communities with thousands of users. The application offers a responsive web user interface, allows sharing local directories, searching for shared files, saving files, chatting capabilities, browsing shared directories, extension support, and a web API for HTTP REST and WebSockets.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.