DPO Fine-Tuning Services for Your Business
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.
How dpo fine tuning works, step by step
A made-up example of one run, from start to finish. Each step is logged so you can see what happened.
- Something happens
A form, an email, a payment or a new row.
- The workflow runs
Steps you agreed, in the right order.
- Errors are caught
Failed steps retry or alert a person.
- You see what ran
A simple log of every run.
Sample Made-up names, data and steps to show how a run is logged. Your steps, tools and rules follow your business.
What you get with dpo fine tuning
Aligning fine-tuned models with enterprise policies via Direct Preference Optimization. Eliminating sycophancy, steering tone, and enforcing brand safety boundaries.
Basic Package
DPO run on 1 model with up to 2k preference pairs. 7-day delivery, 1 round + 30-day fixes.
Standard Package
Preference data creation; DPO, tone/safety evals, up to 8k pairs. 12-day delivery, 1 round + 30-day fixes.
Premium Package
Up to 25k pairs, 2 alignment rounds, red-team tests, 30 days support. 18-day delivery, 1 round + 60-day fixes.
Clear brief first
We confirm what you need before work starts, so the scope is clear on both sides.
Revisions listed
Each package shows its revision rounds. A round is one list of changes sent together.
Yours once paid
Copyright of the final work passes to you once the order is paid in full.
Third-party tools, API usage and hosting are billed to your own accounts, so the setup stays with your business.
How our dpo fine tuning works
Write the policy
We turn your tone and safety rules into examples of preferred and rejected answers.
Build the pairs
Preference data is collected or generated, then checked.
Train with DPO
The model is tuned and compared with the starting point.
Evaluate
Tone and safety tests show whether behaviour changed as intended.
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.
In short
- Delivery in 7 to 18 days, by package.
- Fixed price, shown before you order.
- Teams whose fine-tuned model agrees with everything users say.
- Brands needing a consistent tone and firm limits on what a model may say.
- Copyright yours after full payment.
Dpo Fine Tuning: common questions
How much does dpo fine tuning cost?
Prices are fixed and shown by package on our Dpo Fine Tuning pricing page, so you see the full price before you order. You can also ask for a custom quote.
How long does rlhf consulting take?
Delivery depends on the package: Basic 7-day delivery; Standard 12-day delivery; Premium 18-day delivery.
What is included in the Basic Package?
The Basic Package includes: DPO run on 1 model with up to 2k preference pairs. Higher packages add more scope, revisions and support.
Can I get a custom LLM alignment package?
Yes. If none of the packages fits, use the custom package form on our pricing page to describe what you need, and our team sends you a fixed price.
Is DPO the same as RLHF?
Both use preferences. DPO is simpler, using preference pairs directly rather than a separate reward model, which is why people ask about rlhf consulting services.
Do I need a fine-tuned model first?
Often yes, because DPO refines behaviour on a model that already follows instructions.
Who provides the preference data?
On Standard and Premium, we create it with you.
How do I check the result?
Tone and safety evaluations compare the tuned model with the original.
When should I use DPO instead of more supervised fine tuning?
Use DPO when the model already follows instructions but its style, tone or boundaries are wrong. Supervised tuning is better for teaching new formats or knowledge.
Who owns the final work?
Copyright of the final work passes to you once the order is paid in full. Paid fonts, stock files or licences, if used, are licensed in your name.
Ready to start your dpo fine tuning?
Compare the packages and pick the one that fits. Not sure which? Write to us and we will help you choose.