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All Algorithms implemented in Python
Tensors and Dynamic neural networks in Python with strong GPU acceleration
🏡 Open source home automation that puts local control and privacy first.
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
A curated list of awesome Machine Learning frameworks, libraries and software.
Interact with your documents using the power of GPT, 100% privately, no data leaks
Scrapy, a fast high-level web crawling & scraping framework for Python.
Platform to experiment with the AI Software Engineer. Terminal based. NOTE: Very different from https://gptengineer.app
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
A collection of learning resources for curious software engineers
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
LlamaIndex is the leading framework for building LLM-powered agents over your data.
Streamlit — A faster way to build and share data apps.
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: …
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
We have made you a wrapper you can't refuse
We write your reusable computer vision tools. 💜
Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
A modular graph-based Retrieval-Augmented Generation (RAG) system
Code for the paper "Language Models are Unsupervised Multitask Learners"
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.