Fine-tuning LoRA Models in the Elixir Ecosystem Using Axon

This article explains the concept of Low-Rank Adaptation (LoRA) for model fine-tuning, particularly within the Elixir ecosystem using Axon. LoRA aids in fine-tuning large models efficiently by introducing small, trainable matrices, enabling reduced memory consumption and faster training without compromising model performance. It highlights the introduction of 'graph rewriters' in Axon that facilitate more flexible model modifications. The content then describes a step-by-step process to fine-tune a LoRA model, including setting up the environment, preparing datasets, and implementing the necessary training functions. The piece emphasizes how recent advancements in the Elixir ecosystem make it a viable option for people transitioning from Python for machine learning tasks.