PEFT

Definitions

1. PEFT is parameter-efficient fine-tuning methods that update only a small subset of model parameters. It is commonly used for customizing large models without retraining every weight, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to adapter placement, memory footprint, and downstream quality, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.

   In plain English: PEFT is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.

   Example: "We evaluated PEFT in the new model pipeline because the baseline was plateauing; once it was wired into training and evaluation, quality improved enough to justify rolling it into the next release."

Related Terms

• Lemmatization
• Linear Layer
• Linear Probe
• Linear Regression
• Llama
• Logistic Regression
• Logit
• Long Short-Term Memory