QLoRA (Quantized LoRA)
technique technique
LoRA with quantization for maximum memory efficiency
Supported languages
QLoRA combines the LoRA technique with 4-bit quantization to enable fine-tuning of massive language models on very limited hardware. Using NormalFloat4 and Double Quantization, it drastically reduces memory usage while maintaining original model quality.
Concepts
4bit-quantizationnormalfloat4double-quantizationpaged-optimizersmemory-efficient-traininggradient-checkpointing
Pros and Cons
Ventajas
- + Fine-tune 65B+ models on a single 48GB GPU
- + Reduces memory up to 4x more than standard LoRA
- + Maintains 99% of full fine-tuning quality
- + Democratizes access to large LLM fine-tuning
- + Compatible with most popular models
- + Reasonable training time
Desventajas
- - Slightly slower inference due to quantization
- - Additional configuration complexity
- - Some models don't quantize well
- - Requires specific libraries (bitsandbytes)
Casos de Uso
- Fine-tuning 70B models on consumer GPUs
- Training on laptops with GPU
- Rapid experimentation with large models
- Creating custom models on a budget
- Academic research with limited resources