Supervised Fine-Tuning

Definitions

1. Supervised Fine-Tuning is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model adaptation and task-specific optimization, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to compute budget, data quality, and convergence, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.

   In plain English: Supervised Fine-Tuning 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 Supervised Fine-Tuning 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