AI BriefingPEFT Updates LoRA Adapter Merging Feature
Key point
Hugging Face's PEFT library now supports new merging methods that can efficiently combine LoRA adapters.
Details
A new model merging method for LoRA adapters has been added to Hugging Face's PEFT (Parameter-Efficient Fine-Tuning) library. Existing model merging approaches consumed a lot of memory and required downloading the entire checkpoint, but this update makes it possible to efficiently combine multiple adapters derived from the same base model.
The key newly introduced merging methods are as follows:
- Concatenation (cat): A method that applies weights by concatenating LoRA matrices, allowing adapters with different ranks to be merged.
- Linear/Task Arithmetic (linear): A method proposed in the Task Arithmetic paper that performs a weighted sum of the adapters' delta weights. However, all participating adapters must have the same rank.
- SVD (svd): This method performs merging by applying singular value decomposition (SVD) to the delta weights resulting from matrix multiplication.
This update is not limited to LoRA, and there are also plans to extend it to text-to-image generation models in the future.
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