The AI Bootcamp is a comprehensive training program focusing on real-world applications to equip individuals with the skills and knowledge needed to excel as AI engineers. The bootcamp covers topics such as Real-World PyTorch, Machine Learning Projects, Fine-tuning Tiny LLM, Deployment of LLM to Production, AI Agents with GPT-4 Turbo, CrewAI, Llama 3, and more. Participants will learn foundational skills in Python for AI, ML Pipelines, Large Language Models (LLMs), AI Agents, and work on projects like RagBase for private document chat.

llm-resources is a repository providing resources to get started with Large Language Models (LLMs). It includes videos on Neural Networks and LLMs, free courses, prompt engineering guides, explored frameworks, AI assistants, and tips on making RAG work properly. The repository also contains important links and updates related to LLMs, AWS, RAG, agents, model context protocol, and more. It aims to help individuals with a basic understanding of NLP and programming knowledge to explore and utilize LLMs effectively.

HumanLayer is a Python toolkit designed to enable AI agents to interact with humans in tool-based and asynchronous workflows. By incorporating humans-in-the-loop, agentic tools can access more powerful and meaningful tasks. The toolkit provides features like requiring human approval for function calls, human as a tool for contacting humans, omni-channel contact capabilities, granular routing, and support for various LLMs and orchestration frameworks. HumanLayer aims to ensure human oversight of high-stakes function calls, making AI agents more reliable and safe in executing impactful tasks.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

This repository focuses on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). It provides code and resources to augment LLMs with real-time data for dynamic, context-aware applications. The content covers topics such as semantic search, fine-tuning embeddings, building RAG chatbots, evaluating LLMs, and using knowledge graphs in RAG. Prerequisites include Python skills, knowledge of machine learning and LLMs, and introductory experience with NLP and AI models.

This open-source course teaches how to build an advanced RAG and LLM system using LLMOps and ML systems best practices. It helps you create an AI assistant that leverages your personal knowledge base to answer questions, summarize documents, and provide insights. The course covers topics such as LLM system architecture, pipeline orchestration, large-scale web crawling, model fine-tuning, and advanced RAG features. It is suitable for ML/AI engineers and data/software engineers & data scientists looking to level up to production AI systems. The course is free, with minimal costs for tools like OpenAI's API and Hugging Face's Dedicated Endpoints. Participants will build two separate Python applications for offline ML pipelines and online inference pipeline.

A comprehensive repository featuring a curated collection of AI-powered projects and Multi Agent Systems (MAS) built with the Crew AI framework. It provides innovative AI solutions for various domains, including marketing automation, health planning, legal advice, and more. Users can explore and deploy AI agents, Multi Agent Systems, and advanced machine learning techniques through a diverse selection of projects that leverage state-of-the-art AI technologies like RAG (Retrieval-Augmented Generation). The projects offer practical applications and customizable solutions for integrating AI into existing workflows or new projects.

A curated list of Generative AI projects, tools, artworks, and models

Sarvadnya is a repository focused on interfacing custom data using Large Language Models (LLMs) through Proof-of-Concepts (PoCs) like Retrieval Augmented Generation (RAG) and Fine-Tuning. It aims to enable domain adaptation for LLMs to answer on user-specific corpora. The repository also covers topics such as Indic-languages models, 3D World Simulations, Knowledge Graphs Generation, Signal Processing, Drones, UAV Image Processing, and Floor Plan Segmentation. It provides insights into building chatbots of various modalities, preparing videos, and creating content for different platforms like Medium, LinkedIn, and YouTube. The tech stacks involved range from enterprise solutions like Google Doc AI and Microsoft Azure Language AI Services to open-source tools like Langchain and HuggingFace.

KitOps is a CNCF open standards project for packaging, versioning, and securely sharing AI/ML projects. It provides a unified solution for packaging, versioning, and managing assets in security-conscious enterprises, governments, and cloud operators. KitOps elevates AI artifacts to first-class, governed assets through ModelKits, which are tamper-proof, signable, and compatible with major container registries. The tool simplifies collaboration between data scientists, developers, and SREs, ensuring reliable and repeatable workflows for both development and operations. KitOps supports packaging for various types of models, including large language models, computer vision models, multi-modal models, predictive models, and audio models. It also facilitates compliance with the EU AI Act by offering tamper-proof, signable, and auditable ModelKits.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

RapidRAG is a project focused on Knowledge QA with LLM, combining Questions & Answers based on local knowledge base with a large language model. The project aims to provide a flexible and deployment-friendly solution for building a knowledge question answering system. It is modularized, allowing easy replacement of parts and simple code understanding. The tool supports various document formats and can utilize CPU for most parts, with the large language model interface requiring separate deployment.

Animal-Artificial Intelligence (Animal-AI) is an interdisciplinary research platform designed to understand human, animal, and artificial cognition. It supports AI research to unlock cognitive capabilities and explore the space of possible minds. The open-source project facilitates testing across animals, humans, and AI, providing a comprehensive AI environment with a library of 900 tasks. It offers compatibility with Windows, Linux, and macOS, supporting Python 3.6.x and above. The environment utilizes Unity3D Game Engine, Unity ML-Agents toolkit, and provides interactive elements for AI training scenarios.

LearnHouse is an open-source platform that allows anyone to easily provide world-class educational content. It supports various content types, including dynamic pages, videos, and documents. The platform is still in early development and should not be used in production environments. However, it offers several features, such as dynamic Notion-like pages, ease of use, multi-organization support, support for uploading videos and documents, course collections, user management, quizzes, course progress tracking, and an AI-powered assistant for teachers and students. LearnHouse is built using various open-source projects, including Next.js, TailwindCSS, Radix UI, Tiptap, FastAPI, YJS, PostgreSQL, LangChain, and React.

Mini LLM Flow is a 100-line minimalist LLM framework designed for agents, task decomposition, RAG, etc. It aims to be the framework used by LLMs, focusing on high-level programming paradigms while stripping away low-level implementation details. It serves as a learning resource and allows LLMs to design, build, and maintain projects themselves.

A curated list of Machine Learning (ML) and Generative AI (GenAI) packages and resources for the Elixir programming language. It includes core tools for data exploration, traditional machine learning algorithms, deep learning models, computer vision libraries, generative AI tools, livebooks for interactive notebooks, and various resources such as books, videos, and articles. The repository aims to provide a comprehensive overview for experienced Elixir developers and ML/AI practitioners exploring different ecosystems.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.