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A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally.

LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization

Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications.

MSLK (Meta Superintelligence Labs Kernels) is a collection of PyTorch GPU operator libraries that are designed and optimized for GenAI training and inference, such as FP8 row-wise quantization and collective communications.

Books related to AI/ML/DL/GENAI

EdgeChains.js is Full-Stack GenAI library. Front-end, backend, apis, prompt management, distributed computing. All core prompts & chains are managed declaratively in jsonnet (and not hidden in classes)

Showcasing Google Cloud's generative AI for marketing scenarios via application frontend, backend, and detailed, step-by-step guidance for setting up and utilizing generative AI tools, including examples of their use in crafting marketing materials like blog posts and social media content, nl2sql analysis, and campaign personalization.

[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code

Automatable GenAI Scripting

Open-source observability for your GenAI or LLM application, based on OpenTelemetry

Instant no code DATA API platform for relational databases. Connect any database, run anywhere. Power your GENAI application function/tools calls in seconds.

Build fast and accurate GenAI apps with GraphRAG SDK at scale 🌟

MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.

Generative AI Examples is a collection of GenAI examples such as ChatQnA, Copilot, which illustrate the pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project.

This repository contains sample code demonstrating various use cases leveraging Amazon Bedrock and Generative AI. Each sample is a separate project with its own directory, and includes a basic Streamlit frontend to help users quickly set up a proof of concept.

Open source project for data preparation for GenAI applications

GenAI components at micro-service level; GenAI service composer to create mega-service

Rust multiprovider generative AI client (Ollama, OpenAi, Anthropic, Gemini, DeepSeek, ZAI, OpenRouter, FireworksAI, xAI/Grok, Groq,, ...)

Cost-efficient and pluggable Infrastructure components for GenAI inference

Monocle is a framework for tracing GenAI app code. This repo contains implementation of Monocle for GenAI apps written in Python.

Open GenAI Stack

Welcome to the AWS First Generative AI Journey repository! This project serves as a comprehensive resource for individuals and organizations looking to explore the transformative capabilities of Generative AI using Amazon Web Services (AWS).

Python toolkit for building graph-enhanced GenAI applications

Google Gen AI Java SDK provides an interface for developers to integrate Google's generative models into their Java applications.

The Lodash for GenAI: Real Value + Consistent + Model-Agnostic

GenAI inference performance benchmarking tool

Very simple GenAI application to try the Docker Model Runner

c4 GenAI Suite

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.