implementing-llms-litgpt

LoRA and QLoRA fine-tuning are supported through clean, production-ready LitGPT workflows that provide end-to-end training recipes. You can rapidly customize pretrained models using adapter-based configurations to reduce computational requirements while maintaining model performance.

Over 20 pretrained LLM architectures are available with clean, readable code and end-to-end training recipes. These implementations enable consistent development and deployment across diverse research and engineering workloads.

Yes, LitGPT supports both pretraining and straightforward deployment paths for specialized LLMs. It provides end-to-end capabilities including model loading, training configurations, evaluation hooks, and deployment export with ready-to-use examples.

The quickest way to start building and deploying custom LLM workflows is to install LitGPT, download a model from the Lightning model zoo, and run a quick fine-tune or deployment workflow. This provides production-ready implementations for rapid prototyping.

Yes, LitGMT supports adapter-based customization for existing pretrained models. It provides clean implementations and training workflows that enable consistent development, tuning, and deployment across 20+ architectures with ready-to-use examples.