Individual skill
lora
Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
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# Using LoRA for Fine-tuning
LoRA (Low-Rank Adaptation) enables efficient fine-tuning by freezing pretrained weights and injecting small trainable matrices into transformer layers. This reduces trainable parameters to ~0.1% of the original model while maintaining performance.
## Table of Contents
- [Core Concepts](#core-concepts)
- [Basic Setup](#basic-setup)
- [Configuration Parameters](#configuration-parameters)
- [QLoRA (Quantized LoRA)](#qlora-quantized-lora)
- [Training Patterns](#training-patterns)
- [Saving and Loading](#saving-and-loading)
- [Merging Adapters](#merging-adapters)
- [Best Practices](#best-practices)