The official gpt4free repository | various collection of powerful language models

ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。

🔮 ChatGPT Desktop Application (Mac, Windows and Linux)

🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo

🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.

Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation

The Reliable USB Formatting Utility

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain

MindsDB connects AI models to real time data

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

Chatbox is a desktop client for ChatGPT, Claude and other LLMs, available on Windows, Mac, Linux

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

GPT-powered chat for documentation, chat with your documents

An Open-Source Assistants API and GPTs alternative. Dify.AI is an LLM application development platform. It integrates the concepts of Backend as a Service and LLMOps, covering the core tech stack required for building generative AI-native applications, including a built-in RAG engine.

Draw a mockup and generate html for it

A high-throughput and memory-efficient inference and serving engine for LLMs

The open-source hub to build & deploy GPT/LLM Agents ⚡️

Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform

RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.