🦜🔗 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.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

12 Weeks, 24 Lessons, AI for All!

Data Engineering Zoomcamp is a free nine-week course that covers the fundamentals of data engineering.

Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…

A guidance language for controlling large language models.

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

StableLM: Stability AI Language Models

Free MLOps course from DataTalks.Club

Examples and guides for using the Gemini API

Learn ML engineering for free in 4 months!

CoreNet: A library for training deep neural networks

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" (ICLR 2024)

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Instruct-tune LLaMA on consumer hardware

Analytical chemistry and epidemiology of street drugs

This repository contains an application designed to recommend scientific papers that are most similar to a given input paragraph. The application uses the llama and weaviate libraries to achieve this.

RAG example using the Weaviate vector database

E2E LLM Ops: Cohere + LangChain (LangSmith) + Weaviate