How to implement adapter fusion in QLoRA for multi-task learning

Question

Can i know How to implement adapter fusion in QLoRA for multi-task learning?

Answer 1

You can implement adapter fusion in QLoRA for multi-task learning using the peft library by loading and merging multiple task-specific LoRA adapters.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• PeftModel.from_pretrained(): Loads separately fine-tuned LoRA adapters.

• fuse_lora_weights(): Merges multiple adapters into a unified model.

• Supports multi-task learning: Enables shared knowledge across tasks.

• Seamless inference: Use fused model like any standard Hugging Face model.

Hence, adapter fusion in QLoRA allows effective multi-task learning by merging multiple lightweight task-specific adapters, enabling scalable and modular fine-tuning across diverse NLP tasks without retraining the entire model.