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🦜🔗 Build context-aware reasoning applications
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
A simple screen parsing tool towards pure vision based GUI agent
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
A multi-voice TTS system trained with an emphasis on quality
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
Examples and guides for using the Gemini API
Anthropic's educational courses
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑🔬
This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive A…
Code samples used on cloud.google.com
A course on aligning smol models.
A collection of scientific methods, processes, algorithms, and systems to build stories & models.
Jupyter Notebooks to help you get hands-on with Pinecone vector databases
This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.
Chat with any PDF. Easily upload the PDF documents you'd like to chat with. Instant answers. Ask questions, extract information, and summarize documents with AI. Sources included.
This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.
An example of multi-agent orchestration with llama-index
A reimplementation of langgraph's customer support example in Rasa's CALM paradigm and a quantiative evaluation of the 2 approaches