This repository curates resources and research on the building blocks of efficient and performant foundation models, targeting AI researchers and engineers. It aims to provide a comprehensive overview of advancements in areas like hardware-aware algorithms, attention alternatives, and efficient inference systems, fostering a deeper understanding of AI systems.

How It Works

The project acts as a curated knowledge base, organizing a vast collection of papers, blog posts, courses, and code related to foundation model efficiency. It categorizes these resources by key research areas, such as "Can We Replace Attention?" and "Quantization, Pruning, and Distillation," highlighting canonical texts and recent breakthroughs. This structured approach facilitates exploration and learning for those interested in optimizing AI model performance and resource utilization.

Quick Start & Requirements

This repository is a collection of links and resources, not a runnable software package. No installation or specific requirements are needed to browse its content.

Highlighted Details

• Extensive coverage of hardware-aware algorithms, including foundational texts on I/O complexity and computer architecture.
• Deep dives into alternatives to quadratic attention mechanisms, featuring state-space models (Mamba, S4) and linear attention approximations.
• Detailed sections on model compression techniques like quantization, pruning, and distillation, with links to key papers (QLoRA, Deep Compression).
• Resources dedicated to systems for efficient inference and high-throughput processing, including vLLM and FlexGen.

Maintenance & Community

The project originated from materials for a NeurIPS keynote by Chris Ré and welcomes community contributions via issues or pull requests. Links to relevant courses and blog posts are provided for further engagement.

Licensing & Compatibility

The licensing of individual resources within the repository varies, as it is a curated collection of external links. Users should consult the licenses of the linked papers, code repositories, and courses.

Limitations & Caveats

The repository is described as having a "biased view" and is not exhaustive, encouraging community input to fill gaps. It is a knowledge aggregation project, not a deployable system, and thus has no direct operational limitations.