ArcticTraining documentation
ArcticTraining is a Python library designed to make prototyping and experimenting with LLM training workflows as simple, flexible, and efficient as possible. Whether you’re fine-tuning models with standard approaches or exploring cutting-edge training algorithms, ArcticTraining provides the tools and structure to streamline your work and focus on what matters most.
Why ArcticTraining?
At the heart of ArcticTraining is a modular design built around building blocks—factory objects that handle the creation and management of training assets like models, datasets, optimizers, and checkpoint engines. These components are connected through well-defined interfaces that ensure compatibility and make it easy to swap, customize, or extend any part of the training workflow.
To further reduce boilerplate and simplify customization, ArcticTraining includes a robust callback system that wraps core class methods. This system allows you to tweak and extend functionality without modifying the underlying code, making complex adjustments straightforward and manageable.
Key Features
Extensible Building Blocks: Core components like trainers, model factories, and data sources come with narrow, well-defined interfaces, making it easy to reuse and customize them for your specific needs.
Powerful Callback System: Extend or modify behavior by injecting custom logic into key points of the training workflow without rewriting existing classes.
Seamless CLI Integration: Define training configurations in a YAML file and run them with a simple command, optionally including custom Python code for advanced use cases.
Pre-Built Trainers: Start with examples like the Supervised Fine-Tuning (SFT) trainer, which showcases how minimal changes can create a fully functional training pipeline.
Rapid Prototyping: Experiment with new training algorithms by swapping components or redefining specific methods, all while ensuring compatibility across the workflow.
Quick Start
To get started with ArcticTraining check out the quick start guide