Lorax
A JAX transform that implements LoRA (Low-Rank Adaptation) for efficient fine-tuning of large models with minimal memory overhead.
What is Lorax?
Lorax is a JAX library that implements LoRA (Low-Rank Adaptation), a parameter-efficient fine-tuning method for large machine learning models. It transforms existing JAX model functions to replace weight updates with trainable low-rank matrices, drastically reducing memory consumption during training. This allows fine-tuning of massive models on hardware with limited VRAM, such as consumer GPUs.
Machine learning researchers and engineers working with large JAX models who need to fine-tune them efficiently on memory-constrained hardware. It's particularly useful for those using frameworks like Haiku or HuggingFace Flax.
Lorax stands out by providing a non-invasive, framework-agnostic LoRA implementation for JAX, requiring no modifications to original model code. Its seamless integration, optimizer wrapping, and parameter merging capabilities make it a practical choice for memory-efficient fine-tuning.
Overview
LoRA for arbitrary JAX models and functions
Use Cases
Best For
- Fine-tuning large language models on consumer GPUs with limited VRAM
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