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Parameter-Efficient Tuning

Benched.ai Editorial Team

Parameter-efficient tuning (PET) adapts a large model by training a small subset of parameters—adapters, biases, LoRA ranks—reducing compute and storage.

  PET Methods

MethodExtra ParamsGPU Speed
LoRA0.5–2 %Fast
IA30.05 %Very fast
BitFit0.02 %Fast
Prefix tuning0.1 %Medium

  Memory Savings (7B model)

Training RegimeVRAM
Full 7B FP1632 GB
LoRA r=88 GB
BitFit6 GB

  Design Trade-offs

  • Lower params = cheaper but may cap quality.
  • PET requires merging adapters at inference or extra matmuls.
  • Some licenses disallow weight merging.

  Current Trends (2025)

  • QLoRA + LoRA stack reduces VRAM further with 4-bit base weights.
  • Adapter fusion merges multiple task adapters on-the-fly.
  • PEFT libraries standardize APIs across frameworks.

  Implementation Tips

  1. Start with LoRA rank 8; adjust after eval.
  2. Use 2-stage tuning: LoRA then bias-only for final polish.
  3. Store adapters separately for lightweight distribution.