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"LightRAG: Simple and Fast Retrieval-Augmented Generation"
This is the official github repo of Think-on-Graph (ICLR 2024). If you are interested in our work or willing to join our research team in Shenzhen, please feel free to contact us by email (xuchengj…
Official Implementation of ICLR 2024 paper: "Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning"
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Codes and Data for ACL 2024 Paper "Faithful Logical Reasoning via Symbolic Chain-of-Thought".
A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
The Generative AI Landscape - A Collection of Awesome Generative AI Applications
A distributed, fast open-source graph database featuring horizontal scalability and high availability
A LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
A flexible and efficient deep neural network (DNN) compiler that generates high-performance executable from a DNN model description.
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
这是我学习 PyTorch 的笔记对应的代码，点击查看 PyTorch 笔记在线电子书
🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.
State-of-the-art Parameter-Efficient MoE Fine-tuning Method
《Machine Learning Systems: Design and Implementation》- Chinese Version
Training and serving large-scale neural networks with auto parallelization.