QLoRA
Also known as: Quantized LoRA
LoRA combined with 4-bit base-model quantization, letting you fine-tune huge models on a single consumer GPU.
A fine-tuning method that combines 4-bit quantization of the frozen base model with LoRA adapters. Cuts memory by ~3x vs LoRA, making it feasible to fine-tune 65B-parameter models on a single 48GB GPU.
In practice
How most hobbyists and small teams fine-tune large open models. Expect questions on NF4 vs INT4, double quantization, and quality vs full LoRA.
How it compares
LoRA leaves the base model in FP16; QLoRA additionally quantizes the base model to 4-bit.
Related topics
Related terms
Fine-tuning
Continue training a pre-trained model on task-specific data to specialize its behavior.
LoRA (Low-Rank Adaptation)
Fine-tune by training small low-rank update matrices instead of every weight: same quality, ~1% of the cost.
DPO (Direct Preference Optimization)
Skip the reward model and PPO: fine-tune directly on preferred-vs-rejected response pairs.
Knowledge Distillation
Train a small student model to match a big teacher's outputs: cheap, fast inference with most of the quality.
Pre-training
Self-supervised next-token training on a huge unlabeled corpus, producing the base model.
Supervised Fine-Tuning (SFT)
Fine-tune the base model on (prompt, ideal response) pairs; the first post-training step before alignment.