peft-fine-tuning

Fine-tune large language models on limited GPU memory by training small adapters instead of updating full model weights. Using parameter-efficient methods like LoRA and QLoRA reduces storage and compute costs for 7B-70B models.

LoRA trains low-rank adapters to adapt large models, while QLoRA applies quantization to further reduce memory requirements. QLoRA enables fine-tuning large models on consumer GPUs by loading quantized base weights alongside trainable adapter parameters.

Yes, parameter-efficient fine-tuning integrates directly with Hugging Face transformers, peft, torch, and bitsandbytes. This integration supports quantized fine-tuning and adapter training workflows for large language models.

Manage multiple adapters by saving, loading, merging, and switching them for multi-task serving or deployment. This workflow supports keeping individual adapters for fast deployment or merging and quantizing them for production inference.

Yes, the approach covers advanced methods including AdaLoRA, IA3, Prefix/Prompt tuning, and LoftQ initialization. It also supports techniques like gradient checkpointing and CPU offloading for efficient training.

Fine-tune the 8B model with LoRA for your target domain, save the adapter for fast deployment, and merge with quantization for production inference. This workflow supports rapid iteration and integration with vLLM and TRL.