LoRA Fine-Tuning Services for Your Business
LoRA fine tuning services adapt a large model by training a small set of extra weights, which needs far less computing power than retraining everything. Omeiro applies LoRA and QLoRA to Llama 3, Mistral and Qwen so they handle legal, medical or financial vocabulary.
How lora 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 lora fine tuning
Parameter-Efficient Fine-Tuning (PEFT) on Llama 3, Mistral, and Qwen. Adapting model weights to specialized legal, medical, or financial terminology with reduced compute overhead.
Basic Package
QLoRA fine-tune of 1 model (7-8B) on your dataset up to 2k examples. 5-day delivery, 1 round + 30-day fixes.
Standard Package
Up to 10k examples, data cleaning, eval vs base model, adapter files. 10-day delivery, 1 round + 30-day fixes.
Premium Package
Up to 50k examples, 2 models compared, deployment endpoint, 30 days support. 16-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 lora fine tuning works
Inspect the data
We check format, balance and errors.
Pick the base
A base model is chosen for the task.
Train the adapter
LoRA or QLoRA runs on your examples.
Test
Outputs are compared with the base model.
LoRA fine tuning: preparing a dataset worth training on
Your dataset determines the ceiling. LoRA fine tuning copies the style and content of its examples, including their mistakes, so inconsistent labels or sloppy answers show up in the tuned model. Before ordering, gather examples in a consistent format, typically an instruction with the ideal response, and remove duplicates and anything confidential. A modest set of carefully checked examples often beats a large noisy one, which is why the cleaning step on Standard matters.
Ask whether fine-tuning is the right tool. If the model merely needs access to current facts, retrieval is usually better, while LoRA fine tuning suits consistent style, format or specialised vocabulary. Plan the evaluation before training: set aside unseen examples and compare the tuned adapter with the base model. Also think about licensing, because Llama, Mistral and Qwen each have their own terms, and about where the adapter will run once training is finished.
In short
- Delivery in 5 to 16 days, by package.
- Fixed price, shown before you order.
- Teams with a labelled dataset and a limited GPU budget.
- Copyright yours after full payment.
Lora Fine Tuning: common questions
How much does lora fine tuning cost?
Prices are fixed and shown by package on our Lora Fine Tuning pricing page, so you see the full price before you order. You can also ask for a custom quote.
How long does qlora fine tuning consulting take?
Delivery depends on the package: Basic 5-day delivery; Standard 10-day delivery; Premium 16-day delivery.
What is included in the Basic Package?
The Basic Package includes: QLoRA fine-tune of 1 model (7-8B) on your dataset up to 2k examples. Higher packages add more scope, revisions and support.
Can I get a custom fine tune llama 3 for business 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.
What is the difference between LoRA and QLoRA?
QLoRA trains the adapter on a quantised base model, which lowers memory needs.
Which models can you tune?
Llama 3, Mistral and Qwen.
Do I get the trained weights?
Standard includes adapter files.
How many examples do I need?
Basic starts at up to 2,000.
Should I fine tune a model or use RAG?
Use retrieval when the model needs up-to-date or private facts, and fine-tuning when it needs a consistent style, format or specialist vocabulary. Some projects combine both.
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 lora fine tuning?
Compare the packages and pick the one that fits. Not sure which? Write to us and we will help you choose.