peft-fine-tuning

Parameter-efficient fine-tuning with QLoRA reduces memory usage by applying adapters to a quantized 70B model, enabling training on memory-constrained consumer GPUs. It updates only a tiny fraction of parameters while preserving model performance.

LoRA applies low-rank adapters to update parameters efficiently, while AdaLoRA dynamically adjusts adapter rank during training to balance the fine-tuning footprint and model quality. Both are supported PEFT methods for adapting large language models.

Yes, multi-adapter serving allows you to load and deploy multiple fine-tuned variants from a single base model. This workflow supports task-specific tuning and evaluation without duplicating the entire model weights in memory.

Yes, the PEFT workflow requires the bitsandbytes and transformers stack along with torch and peft packages. These dependencies provide the quantization and model integration needed for memory-efficient fine-tuning of 7B-70B models.

IA3 updates even fewer parameters than LoRA by scaling activations, offering a smaller training footprint when memory is highly constrained. Choose IA3 for extreme memory efficiency or LoRA when balancing footprint and adaptation quality.

QLoRA combines quantization with LoRA adapters to drastically reduce memory usage during fine-tuning. By updating only a tiny fraction of parameters on a quantized base model, it enables training large language models on memory-constrained GPUs.