This repository serves as a comprehensive guide and collection of practical resources for deep learning practitioners, particularly those working with large language models (LLMs). It aims to demystify complex training and inference processes by providing detailed explanations, utilities, and curated links to external tools and papers, targeting beginners and experienced engineers alike.

How It Works

The Cookbook organizes information into distinct sections covering LLM calculations, benchmarks, foundational concepts, distributed training, best practices, and educational code examples. It leverages a curated approach, linking to external calculators, visualizations, and annotated papers to explain core concepts like FLOPs, VRAM estimation, and parallelism strategies. The project also includes minimal code repositories for pedagogical purposes, demonstrating key LLM architectures.

Quick Start & Requirements

This repository is primarily a collection of curated links and explanations, not a runnable application. No direct installation or execution commands are provided. Users will need to follow links to external resources for specific tools or code examples.

Highlighted Details

• Provides utilities for calculating FLOPs, memory overhead, and parameter counts for LLMs.
• Includes benchmarks for communication and transformer sizing.
• Features a reading list with annotated papers and visualizations for understanding transformer architecture.
• Offers minimal code repositories for pedagogical purposes, covering various LLM architectures.

Maintenance & Community

The project is associated with EleutherAI, a prominent research collective in the AI space. Contributions are welcomed via GitHub Issues and Pull Requests.

Licensing & Compatibility

The repository itself is not directly licensed for use as a software package. The content is intended for educational purposes. Specific licenses for linked external code or papers would need to be consulted individually.

Limitations & Caveats

As a curated collection of resources, this repository does not provide a unified, runnable framework. Users must navigate and integrate various external tools and codebases, each with its own dependencies and setup requirements.