Dpo Fine Tuning Services
Packages & Pricing.

DPO fine tuning services to align models with your policies

DPO fine tuning services teach a model which of two answers you prefer, using pairs of good and bad responses. Omeiro uses Direct Preference Optimization to reduce sycophancy, steer tone and enforce brand and safety boundaries.

Standard and Premium also create the preference data, run tone and safety evaluations and finish with red-team tests.

DPO fine tuning: what a good preference dataset looks like

Preferences must be consistent. DPO fine tuning only works when the chosen answer in each pair is better for a reason the model can learn, such as being more direct, more polite or safer, so reviewers need written guidelines. Mixed signals, where one reviewer rewards length and another punishes it, produce a muddled model. Before ordering, describe behaviours you want less of, such as flattery or over-apologising, and supply examples of answers you would be glad to see.

Pairs can also introduce unwanted habits. If chosen answers are always longer, the model may simply learn to ramble, so balance length and style across the dataset. DPO fine tuning usually comes after supervised tuning, so tell us which base model and prior training you have. Hold back a set of test prompts, including some adversarial ones, so tone and safety checks show whether behaviour changed as intended and not only on training topics.