fine-tuning-hub
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#
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• A stable, repeated behavior resists prompt-only fixes
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• You have representative examples and a trustworthy evaluation set
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• Shorter prompts or a smaller model may improve serving economics
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• The chosen checkpoint and artifacts can be deployed and governed
Reasons to use another approach#
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• Use retrieval or tools for changing or attributable knowledge
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• Improve prompts when the desired behavior fits clear instructions
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• Fix the product or data pipeline when the model lacks required context
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• Delay training when data rights, quality, or evaluation are unresolved
A release-oriented workflow#
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1 Write the product goal, acceptance rubric, and failure budget.
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2 Create a sealed holdout from production-shaped success, failure, and safety cases.
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3 Measure prompt-only, few-shot, retrieval, and tool-use baselines.
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4 Select a licensed base model and the smallest practical adaptation method.
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5 Run a short, reproducible pilot and inspect learning curves and sample outputs.
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6 Compare quality, regressions, latency, throughput, and total serving cost.
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7 Canary the winning candidate with monitoring and a tested rollback path.