PEFT

PEFT, short for Parameter-Efficient Fine-Tuning, is the umbrella term for a family of techniques that adapt large pretrained models by training only a tiny fraction of their parameters — typically 0.01% to 1% — while keeping the bulk of the network frozen. The canonical PEFT methods include LoRA, QLoRA, prefix tuning, prompt tuning, adapter layers, and IA3, each making different choices about where in the architecture to inject the trainable parameters. PEFT dramatically reduces training cost, storage cost (small adapters versus full model copies), and risk of catastrophic forgetting compared to full fine-tuning. The Hugging Face PEFT library, released in 2023, has become the standard implementation. PEFT also enables modular composition where many task-specific adapters can be combined or switched at inference time on a single base model. AI governance teams favor PEFT for domain adaptation because the small adapter sizes are easy to audit, version control, and revert if regressions are detected. Most production LLM fine-tuning today uses PEFT rather than full fine-tuning.

PEFT lifecycle governance in Centralpoint: Centralpoint supports PEFT-adapted models from any base — Llama, Mistral, Qwen, OpenAI fine-tuned variants — under one model-agnostic governance layer. The platform meters tokens per skill, keeps prompts local, supports both generative and embedded models, and deploys adapter-routed chatbots through one line of JavaScript with full audit trails.


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