This handbook provides a technical, hands-on collection of methodologies for training large language models, targeting LLM training engineers and operators. It offers scripts and commands for practical problem-solving, complementing a conceptual overview found in the sister "Playbook."

How It Works

The handbook focuses on practical implementation details for LLM training, covering essential aspects like model parallelism, throughput maximization, tensor precision, hyperparameter tuning, initialization strategies, instability debugging, and both software and hardware failure resolution. The approach emphasizes actionable scripts and copy-paste commands for immediate application.

Quick Start & Requirements

• Install: No explicit installation instructions are provided; the content is primarily documentation and code snippets.
• Prerequisites: Assumes familiarity with LLM training concepts and environments. Specific code snippets may require Python, deep learning frameworks (e.g., PyTorch, TensorFlow), and potentially cluster management tools like SLURM.
• Resources: No specific resource requirements are detailed, but the content implies the need for significant computational resources typical for LLM training.
• Links: The Large Language Model Training Playbook (sister resource)

Highlighted Details

• Comprehensive coverage of model parallelism techniques.
• Strategies for maximizing training throughput.
• Guidance on tensor precision and data types.
• Debugging methodologies for software and hardware failures.

Maintenance & Community

The project is an open collection, implying community contributions are welcome. Specific contributors, sponsorships, or community channels are not detailed in the provided text.

Licensing & Compatibility

• Content: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
• Code: Apache License, Version 2.0 (unless specified otherwise).
• Compatibility: CC BY-SA 4.0 requires derivative works to be shared under the same license. Apache 2.0 is permissive for commercial use and closed-source linking.

Limitations & Caveats

The handbook is a work in progress, with the list of topics expanding over time and currently covering only a subset of LLM training methodologies.