QLoRA Strategy¶
Memory-efficient fine-tuning using Quantized Low-Rank Adaptation.
Overview¶
QLoRA combines 4-bit quantization with LoRA to dramatically reduce memory usage while maintaining quality.
Features¶
✅ 30-50% less memory - Train larger models on smaller GPUs
✅ Minimal quality loss - Nearly identical to full precision
✅ Compatible with all models - Works with any model
✅ Production ready - Well-tested and stable
Usage¶
{
"strategy": "qlora",
"use_4bit": true,
"bnb_4bit_compute_dtype": "float16",
"bnb_4bit_quant_type": "nf4"
}
Benefits¶
Memory Comparison (Llama-3.2-7B)¶
| Strategy | VRAM Usage |
|---|---|
| Standard SFT | 16 GB |
| QLoRA | 10 GB |
| Savings | 37.5% |
When to Use¶
Use QLoRA when:
✅ Limited VRAM (< 12GB for 7B models)
✅ Want to train larger models
✅ Memory is the bottleneck
✅ Quality is still important
Next Steps¶
- SFT Strategy - Standard approach
- Strategy Overview - Compare strategies
QLoRA: Train bigger models on smaller GPUs! 💾