Model Fine-Tuning
Adapt suitable models to a defined task, domain, or response behavior using representative examples.
Decide whether fine-tuning is needed
Risenine offers model fine-tuning for tasks where adapting a suitable model may improve its behavior. Fine-tuning updates model parameters using training examples, unlike RAG, which supplies retrieved information at answer time.
A clear task definition comes first. Prompting, retrieval, or a different model may address the requirement without additional training. Fine-tuning should be chosen when there is an identifiable behavior to improve and appropriate data to evaluate it.
Prepare data and compare a baseline
Training examples should reflect the actual task and expected output. Dataset quality, permitted use, and separation between training and evaluation material are essential to interpreting the results.
The engagement scope should establish the base model, expected behavior, constraints, and baseline performance. Model provider, hosting, training size, and cost depend on the project and are not fixed promises across all engagements.
Validate the adapted behavior
Evaluation should compare the adapted model with the baseline on relevant examples and difficult cases. It should also check whether improvements to the target behavior introduce unwanted behavior elsewhere.
Fine-tuning is a stated Risenine capability; the public portfolio does not yet identify a standalone fine-tuning case study with measured results. Share your task and available examples to discuss an appropriate evaluation plan.
Discuss your AI project.
Share the problem, available data, and workflow you want to improve.
Discuss your project