DeepAI is a proxy server that enhances the interaction experience of large language models (LLMs) by integrating the 'thinking chain' process. It acts as an intermediary layer, receiving standard OpenAI API compatible requests, using independent 'thinking services' to generate reasoning processes, and then forwarding the enhanced requests to the LLM backend of your choice. This ensures that responses are not only generated by the LLM but also based on pre-inference analysis, resulting in more insightful and coherent answers. DeepAI supports seamless integration with applications designed for the OpenAI API, providing endpoints for '/v1/chat/completions' and '/v1/models', making it easy to integrate into existing applications. It offers features such as reasoning chain enhancement, flexible backend support, API key routing, weighted random selection, proxy support, comprehensive logging, and graceful shutdown.

BlueLM is a large-scale pre-trained language model developed by vivo AI Global Research Institute, featuring 7B base and chat models. It includes high-quality training data with a token scale of 26 trillion, supporting both Chinese and English languages. BlueLM-7B-Chat excels in C-Eval and CMMLU evaluations, providing strong competition among open-source models of similar size. The models support 32K long texts for better context understanding while maintaining base capabilities. BlueLM welcomes developers for academic research and commercial applications.

HivisionIDPhoto is a practical algorithm for intelligent ID photo creation. It utilizes a comprehensive model workflow to recognize, cut out, and generate ID photos for various user photo scenarios. The tool offers lightweight cutting, standard ID photo generation based on different size specifications, six-inch layout photo generation, beauty enhancement (waiting), and intelligent outfit swapping (waiting). It aims to solve emergency ID photo creation issues.

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.

LangChain-Chatchat is an open-source, offline-deployable retrieval-enhanced generation (RAG) large model knowledge base project based on large language models such as ChatGLM and application frameworks such as Langchain. It aims to establish a knowledge base Q&A solution that is friendly to Chinese scenarios, supports open-source models, and can run offline.

Kirara AI is a chatbot that supports mainstream large language models and chat platforms. It provides features such as image sending, keyword-triggered replies, multi-account support, personality settings, and support for various chat platforms like QQ, Telegram, Discord, and WeChat. The tool also supports HTTP server for Web API, popular large models like OpenAI and DeepSeek, plugin mechanism, conditional triggers, admin commands, drawing models, voice replies, multi-turn conversations, cross-platform message sending, custom workflows, web management interface, and built-in Frpc intranet penetration.

Speech-AI-Forge is a project developed around TTS generation models, implementing an API Server and a WebUI based on Gradio. The project offers various ways to experience and deploy Speech-AI-Forge, including online experience on HuggingFace Spaces, one-click launch on Colab, container deployment with Docker, and local deployment. The WebUI features include TTS model functionality, speaker switch for changing voices, style control, long text support with automatic text segmentation, refiner for ChatTTS native text refinement, various tools for voice control and enhancement, support for multiple TTS models, SSML synthesis control, podcast creation tools, voice creation, voice testing, ASR tools, and post-processing tools. The API Server can be launched separately for higher API throughput. The project roadmap includes support for various TTS models, ASR models, voice clone models, and enhancer models. Model downloads can be manually initiated using provided scripts. The project aims to provide inference services and may include training-related functionalities in the future.

Chuanhu Chat is a user-friendly web graphical interface that provides various additional features for ChatGPT and other language models. It supports GPT-4, file-based question answering, local deployment of language models, online search, agent assistant, and fine-tuning. The tool offers a range of functionalities including auto-solving questions, online searching with network support, knowledge base for quick reading, local deployment of language models, GPT 3.5 fine-tuning, and custom model integration. It also features system prompts for effective role-playing, basic conversation capabilities with options to regenerate or delete dialogues, conversation history management with auto-saving and search functionalities, and a visually appealing user experience with themes, dark mode, LaTeX rendering, and PWA application support.

Awesome-ChatTTS is an official recommended guide for ChatTTS beginners, compiling common questions and related resources. It provides a comprehensive overview of the project, including official introduction, quick experience options, popular branches, parameter explanations, voice seed details, installation guides, FAQs, and error troubleshooting. The repository also includes video tutorials, discussion community links, and project trends analysis. Users can explore various branches for different functionalities and enhancements related to ChatTTS.

This repository aims to build a low-parameter LLM model through pretraining, fine-tuning, model rewarding, and reinforcement learning stages to create a chat model capable of simple conversation tasks. It features using the bert4torch training framework, seamless integration with transformers package for inference, optimized file reading during training to reduce memory usage, providing complete training logs for reproducibility, and the ability to customize robot attributes. The chat model supports multi-turn conversations. The trained model currently only supports basic chat functionality due to limitations in corpus size, model scale, SFT corpus size, and quality.

Xianyu AutoAgent is an AI customer service robot system specifically designed for the Xianyu platform, providing 24/7 automated customer service, supporting multi-expert collaborative decision-making, intelligent bargaining, and context-aware conversations. The system includes intelligent conversation engine with features like context awareness and expert routing, business function matrix with modules like core engine, bargaining system, technical support, and operation monitoring. It requires Python 3.8+ and NodeJS 18+ for installation and operation. Users can customize prompts for different experts and contribute to the project through issues or pull requests.

This is a Chinese AI/LLM introductory project that aims to help students overcome the initial difficulties of accessing foreign large models' APIs. The project uses the OpenAI SDK to provide a more compatible learning experience. It covers topics such as AI video summarization, LLM fine-tuning, and AI image generation. The project also offers a CodePlayground for easy setup and one-line script execution to experience the charm of AI. It includes guides on API usage, LLM configuration, building AI applications with Gradio, customizing prompts for better model performance, understanding LoRA, and more.

DISC-LawLLM is a legal domain large model that aims to provide professional, intelligent, and comprehensive **legal services** to users. It is developed and open-sourced by the Data Intelligence and Social Computing Lab (Fudan-DISC) at Fudan University.

Chatluna is a machine learning model plugin that provides chat services with large language models. It is highly extensible, supports multiple output formats, and offers features like custom conversation presets, rate limiting, and context awareness. Users can deploy Chatluna under Koishi without additional configuration. The plugin supports various models/platforms like OpenAI, Azure OpenAI, Google Gemini, and more. It also provides preset customization using YAML files and allows for easy forking and development within Koishi projects. However, the project lacks web UI, HTTP server, and project documentation, inviting contributions from the community.

Muice-Chatbot is an AI chatbot designed to proactively engage in conversations with users. It is based on the ChatGLM2-6B and Qwen-7B models, with a training dataset of 1.8K+ dialogues. The chatbot has a speaking style similar to a 2D girl, being somewhat tsundere but willing to share daily life details and greet users differently every day. It provides various functionalities, including initiating chats and offering 5 available commands. The project supports model loading through different methods and provides onebot service support for QQ users. Users can interact with the chatbot by running the main.py file in the project directory.

ChatTTS-Forge is a powerful text-to-speech generation tool that supports generating rich audio long texts using a SSML-like syntax and provides comprehensive API services, suitable for various scenarios. It offers features such as batch generation, support for generating super long texts, style prompt injection, full API services, user-friendly debugging GUI, OpenAI-style API, Google-style API, support for SSML-like syntax, speaker management, style management, independent refine API, text normalization optimized for ChatTTS, and automatic detection and processing of markdown format text. The tool can be experienced and deployed online through HuggingFace Spaces, launched with one click on Colab, deployed using containers, or locally deployed after cloning the project, preparing models, and installing necessary dependencies.

DeepAI is a proxy server that enhances the interaction experience of large language models (LLMs) by integrating the 'thinking chain' process. It acts as an intermediary layer, receiving standard OpenAI API compatible requests, using independent 'thinking services' to generate reasoning processes, and then forwarding the enhanced requests to the LLM backend of your choice. This ensures that responses are not only generated by the LLM but also based on pre-inference analysis, resulting in more insightful and coherent answers. DeepAI supports seamless integration with applications designed for the OpenAI API, providing endpoints for '/v1/chat/completions' and '/v1/models', making it easy to integrate into existing applications. It offers features such as reasoning chain enhancement, flexible backend support, API key routing, weighted random selection, proxy support, comprehensive logging, and graceful shutdown.

LLM Steer is a Python module designed to steer Large Language Models (LLMs) towards specific topics or subjects by adding steer vectors to different layers of the model. It enhances the model's capabilities, such as providing correct responses to logical puzzles. The tool should be used in conjunction with the transformers library. Users can add steering vectors to specific layers of the model with coefficients and text, retrieve applied steering vectors, and reset all steering vectors to the initial model. Advanced usage involves changing default parameters, but it may lead to the model outputting gibberish in most cases. The tool is meant for experimentation and can be used to enhance role-play characteristics in LLMs.

The crewAI Tools repository provides a guide for setting up tools for crewAI agents, enabling the creation of custom tools to enhance AI solutions. Tools play a crucial role in improving agent functionality. The guide explains how to equip agents with a range of tools and how to create new tools. Tools are designed to return strings for generating responses. There are two main methods for creating tools: subclassing BaseTool and using the tool decorator. Contributions to the toolset are encouraged, and the development setup includes steps for installing dependencies, activating the virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. Enhance AI agent capabilities with advanced tooling.

Dwata is a desktop application that allows users to chat with any AI model and gain insights from their data. Chats are organized into threads, similar to Discord, with each thread connecting to a different AI model. Dwata can connect to databases, APIs (such as Stripe), or CSV files and send structured data as prompts when needed. The AI's response will often include SQL or Python code, which can be used to extract the desired insights. Dwata can validate AI-generated SQL to ensure that the tables and columns referenced are correct and can execute queries against the database from within the application. Python code (typically using Pandas) can also be executed from within Dwata, although this feature is still in development. Dwata supports a range of AI models, including OpenAI's GPT-4, GPT-4 Turbo, and GPT-3.5 Turbo; Groq's LLaMA2-70b and Mixtral-8x7b; Phind's Phind-34B and Phind-70B; Anthropic's Claude; and Ollama's Llama 2, Mistral, and Phi-2 Gemma. Dwata can compare chats from different models, allowing users to see the responses of multiple models to the same prompts. Dwata can connect to various data sources, including databases (PostgreSQL, MySQL, MongoDB), SaaS products (Stripe, Shopify), CSV files/folders, and email (IMAP). The desktop application does not collect any private or business data without the user's explicit consent.

BambooAI is a lightweight library utilizing Large Language Models (LLMs) to provide natural language interaction capabilities, much like a research and data analysis assistant enabling conversation with your data. You can either provide your own data sets, or allow the library to locate and fetch data for you. It supports Internet searches and external API interactions.

Sycamore is a conversational search and analytics platform for complex unstructured data, such as documents, presentations, transcripts, embedded tables, and internal knowledge repositories. It retrieves and synthesizes high-quality answers through bringing AI to data preparation, indexing, and retrieval. Sycamore makes it easy to prepare unstructured data for search and analytics, providing a toolkit for data cleaning, information extraction, enrichment, summarization, and generation of vector embeddings that encapsulate the semantics of data. Sycamore uses your choice of generative AI models to make these operations simple and effective, and it enables quick experimentation and iteration. Additionally, Sycamore uses OpenSearch for indexing, enabling hybrid (vector + keyword) search, retrieval-augmented generation (RAG) pipelining, filtering, analytical functions, conversational memory, and other features to improve information retrieval.

Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.

OAD is a powerful open-source tool for analyzing and visualizing data. It provides a user-friendly interface for exploring datasets, generating insights, and creating interactive visualizations. With OAD, users can easily import data from various sources, clean and preprocess data, perform statistical analysis, and create customizable visualizations to communicate findings effectively. Whether you are a data scientist, analyst, or researcher, OAD can help you streamline your data analysis workflow and uncover valuable insights from your data.

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.