Cam SmithAI Studio

Approaches

Parameter-efficient fine-tuning

Train a limited set of parameters or adapters while keeping most base weights fixed.

When to consider it

  • When model adaptation needs lower training and storage cost than full fine-tuning

Considerations

  • Fewer trainable parameters may limit expressiveness; quality is task-dependent.

Connections

Sources

  1. PEFT methods overviewHugging Face · checked 2026-09-30

Last reviewed 2026-09-30