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
- 1Write the product goal, acceptance rubric, and failure budget.
- 2Create a sealed holdout from production-shaped success, failure, and safety cases.
- 3Measure prompt-only, few-shot, retrieval, and tool-use baselines.
- 4Select a licensed base model and the smallest practical adaptation method.
- 5Run a short, reproducible pilot and inspect learning curves and sample outputs.
- 6Compare quality, regressions, latency, throughput, and total serving cost.
- 7Canary the winning candidate with monitoring and a tested rollback path.
Minimum comparison report
Base checkpoint, prompt, retrieval, and tuned candidate on the same holdout
Per-slice task results with sample counts, not only one aggregate score
Safety and base-capability regressions with written release thresholds
Training configuration, seed, code revision, data revision, and artifact hashes
Measured latency, throughput, memory, and cost on the intended serving stack
Named owner, canary plan, monitoring signals, and rollback procedure