qlora

What problem does it solve?

Advanced QLoRA experiments enable efficient fine-tuning of large language models by using low-rank adapters to reduce memory and compute while preserving performance.

Core Features & Use Cases

• Experiment with alpha scaling, LoRA rank, and target modules to tailor adapters for different task requirements.
• Compare multi-adapter hot-swapping and continual learning workflows to support sequential domain adaptation.
• Evaluate quantization strategies (e.g., 4-bit NF4 vs BF16) to balance memory usage and model quality.

Quick Start

Load a base model compatible with PEFT and apply a qlora adapter with default r=16 and lora_alpha=16 to begin comparing target_modules across experiments.