minLoRA is a minimal PyTorch library designed to apply Low-Rank Adaptation (LoRA) to any torch.nn.Module with minimal code modification. It targets researchers and developers looking for a straightforward, flexible, and efficient way to fine-tune large models without altering their core architecture, enabling faster experimentation and deployment.

How It Works

minLoRA leverages PyTorch's torch.nn.utils.parametrize to inject LoRA adapters directly into existing model layers. This functional approach avoids modifying the model's definition, making it universally applicable to any PyTorch module. The library handles training, inference, and even managing multiple LoRA configurations for a single model, offering a clean and extendable solution.

Quick Start & Requirements

• Install: pip install -e . after cloning the repository.
• Requirements: PyTorch.
• Links: Demo notebook, Advanced usage.

Highlighted Details

• Minimal ~100 lines of code.
• Functional, no model definition modification required.
• Utilizes torch.nn.utils.parametrize for seamless integration.
• Supports training, inference, and multiple LoRA models.

Maintenance & Community

The project is a personal implementation by changjonathanc. There are no explicit mentions of community channels or significant contributor activity in the README.

Licensing & Compatibility

The README does not explicitly state a license. Given its minimal nature and lack of explicit licensing, users should exercise caution regarding commercial use or closed-source integration.

Limitations & Caveats

The project is presented as a minimal re-implementation and may lack the robustness, extensive testing, or advanced features found in more comprehensive LoRA libraries. The absence of a specified license poses a significant caveat for adoption.