This repository is the source for a book (or large written work) titled “The State of Open Source AI”. The goal of the project is to bring clarity to the rapidly evolving open-source AI ecosystem by documenting trends, models, tools, standards, deployment practices, and challenges. It acts as both a snapshot and a guide: readers can see what’s “hot now” in open AI infrastructure, what open licensing or governance issues are emerging, how deployment options compare, and what gaps remain. Because the AI domain moves quickly, part of the aim is to make the content maintainable and updateable by the community. The structure includes chapters or sections about model formats, evaluation benchmarks, hardware/backends, MLOps systems, alignment and safety issues, and open datasets. The repository contains the text (in Markdown or similar), configuration for build or publishing (static site or e-book), and contributor guidelines.
Features
- Broad coverage: model training, evaluation metrics, datasets, licensing, deployment tools, etc.
- Glossary of terms so readers unfamiliar with some frameworks/tools can catch up
- Pros/cons discussions for each topic, not just listing tools but contextualizing usefulness/trade-offs
- Examples of real models, their training data, and deployment instances to illustrate concepts
- Up-to-date with recent developments in open source AI (2023)
- Freely accessible book/site; versioned/documented
Project Samples
