LoRA (Low-Rank Adaptation): - Instead of retraining ALL model weights (billions of parameters), train a small 'adapter' layer - Analogy: Adding a turbocharger to an existing engine vs rebuilding the entire engine - Result: Same base model + specialized knowledge layer - Time: Hours instead of weeks - Cost: Single GPU instead of data center
QLoRA (Quantized LoRA): - Same as LoRA but uses 4-bit quantization - A 70B parameter model normally needs 140GB VRAM - With QLoRA: Fits on 24GB GPU - Makes enterprise-grade fine-tuning accessible
Technical Stack: - Hugging Face Transformers - PEFT (Parameter-Efficient Fine-Tuning) - bitsandbytes for quantization - Accelerate for multi-GPU if needed