peft-fine-tuning

Fine-tune large language models on limited GPU memory by training less than 1% of parameters using LoRA and QLoRA workflows. This approach targets 7B-70B scale models, minimizing compute and memory costs while adapting base models to specialized tasks.

Parameter-efficient fine-tuning enables training large language models using small updates, reducing compute and memory costs. It is needed when adapting 7B-70B scale models on constrained GPUs where full-parameter training is infeasible.

Multi-adapter workflows in PEFT enable deploying multiple task-specific variants from a single base model. You can train and manage several LoRA adapters simultaneously, allowing specialized deployments without duplicating the full model weights.

LoRA and QLoRA fine-tuning workflows require PEFT, Transformers, PyTorch, and BitsAndBytes. These libraries integrate seamlessly with HuggingFace Transformers to provide parameter-efficient training capabilities for large language models.

Parameter-efficient fine-tuning integrates seamlessly with HuggingFace Transformers via the PEFT library. This integration streamlines LoRA and QLoRA workflows, allowing you to adjust models with a tiny fraction of parameters directly within familiar Transformers pipelines.

LoRA and QLoRA are both parameter-efficient fine-tuning techniques supported by PEFT. QLoRA further reduces memory requirements by quantizing the base model, enabling fine-tuning of larger models on more constrained GPU environments compared to standard LoRA.