MoRA (Modular Rank-wise Adaptation) is a parameter-efficient fine-tuning (PEFT) technique designed to enhance the efficiency and effectiveness of adapting large language models. It targets researchers and practitioners working with LLMs who need to fine-tune models with fewer parameters and computational resources, offering a more flexible and potentially superior alternative to standard LoRA.

How It Works

MoRA decomposes the low-rank update matrices into multiple smaller, low-rank matrices, allowing for a more granular control over the adaptation process. It introduces two types of updates: Type 1 shares parameters across multiple low-rank matrices for large ranks, reducing parameter count, while Type 6 uses a novel RoPE-based approach for smaller ranks, offering improved performance. This modularity allows for a trade-off between parameter efficiency and expressiveness.

Quick Start & Requirements

• Install via pip install -e ./peft-mora.
• Requires Hugging Face peft library.
• Examples provided for fine-tuning and pretraining using deepspeed.
• Supports bf16 and 16bit precision.

Highlighted Details

• Implemented as an extension to Hugging Face's peft library.
• Supports two distinct update types (Type 1 and Type 6) for different rank scenarios.
• Can be merged into the base model using merge_and_unload().
• Examples demonstrate integration with deepspeed for distributed training.

Maintenance & Community

The project is based on popular libraries like peft, alpaca-lora, and ReLoRA. Further community engagement details (Discord, Slack, roadmap) are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state the license. Given its reliance on Hugging Face peft and alpaca-lora, it is likely compatible with common open-source licenses, but explicit verification is recommended for commercial use.

Limitations & Caveats

The project is presented as an implementation within peft-mora, suggesting it might be an experimental or research-oriented extension rather than a fully integrated feature of the main peft library. The specific performance benefits and stability compared to standard LoRA or other PEFT methods would require further evaluation.