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What problem does it solve?

Parameter-efficient fine-tuning using LoRA, QLoRA, and Unsloth reduces memory and compute by training only small adapter parameters while preserving model performance.

Core Features & Use Cases

• LoRA basics: train low-rank adapters on selected layers to adapt models with minimal parameter updates.
• QLoRA and Unsloth: leverage quantized training and speedups for faster experimentation.
• Practical workflows: domain adaptation, task-specific fine-tuning, rapid prototyping, and reversible adapter deployment.

Quick Start

Train a small LoRA adapter on your base model with your dataset and save the adapters for later loading during inference.