# Fine-Tuning Hub


📂 fine-tuning

## FINE-TUNING GUIDE

# Fine-Tuning Hub

Plan, train, evaluate, and operate model adaptations with explicit trade-offs and safeguards.

## What fine-tuning changes

Fine-tuning updates some or all of a pretrained model’s parameters using task examples or preference feedback. It can improve a measured behavior, such as output structure, tone, or task accuracy, but it does not automatically add fresh knowledge, eliminate hallucinations, or make a model safe.

### Good reasons to test it

- • A stable, repeated behavior resists prompt-only fixes

- • You have representative examples and a trustworthy evaluation set

- • Shorter prompts or a smaller model may improve serving economics

- • The chosen checkpoint and artifacts can be deployed and governed

### Reasons to use another approach

- • Use retrieval or tools for changing or attributable knowledge

- • Improve prompts when the desired behavior fits clear instructions

- • Fix the product or data pipeline when the model lacks required context

- • Delay training when data rights, quality, or evaluation are unresolved

## A release-oriented workflow

- 1
Write the product goal, acceptance rubric, and failure budget.

- 2
Create a sealed holdout from production-shaped success, failure, and safety cases.

- 3
Measure prompt-only, few-shot, retrieval, and tool-use baselines.

- 4
Select a licensed base model and the smallest practical adaptation method.

- 5
Run a short, reproducible pilot and inspect learning curves and sample outputs.

- 6
Compare quality, regressions, latency, throughput, and total serving cost.

- 7
Canary the winning candidate with monitoring and a tested rollback path.

## Minimum comparison report

## Continue with the decision guides

## Fine-Tuning Guide

## Fine-Tuning Guide