Model Engineering

LLM Fine-Tuning Services

LLM fine-tuning adapts a base language model to your specific task, format, or domain by training it on your examples. MobAppAI fine-tunes smaller, cost-efficient models (using LoRA / parameter-efficient methods) for narrow, high-frequency enterprise tasks — with rigorous evaluation.

What fine-tuning is

Fine-tuning continues training a pre-trained model on your own labelled examples so it internalizes a behaviour — a strict output format, a house tone of voice, or a specialised classification task. Unlike prompting, the behaviour becomes part of the model, which means shorter prompts, more consistent outputs, and lower cost per call at high volume.

Fine-tuning vs RAG

They solve different problems. RAG gives a model access to knowledge; fine-tuningteaches it a skill or style. If your challenge is “the model doesn't know our data,” you want RAG. If it's “the model knows enough but won't respond in the exact shape we need, reliably, cheaply,” you want fine-tuning. We compare them in depth in RAG vs fine-tuning.

How we fine-tune

  • Data preparation: assemble, clean, de-duplicate, and label a training set from your real examples — usually the highest- leverage step.
  • Parameter-efficient training (LoRA/PEFT): adapt a smaller open model instead of retraining billions of parameters, cutting cost and turnaround.
  • Evaluation: measure accuracy, format validity, and regressions against a held-out set before anything reaches production.
  • Private deployment: ship the model where you need it — your cloud or on-prem — with full handover.

Good fits for fine-tuning

  • Structured-output tasks that must return valid JSON every time.
  • High-volume classification, extraction, or routing at low cost.
  • A consistent brand voice or domain vocabulary.

Our process

Fine-tuning is part of our broader AI solutions practice. We work in two-week sprints with sandbox builds you can review, and hand over the model, training pipeline, evaluation set, and documentation so you own the result outright.

LLM Fine-Tuning FAQ

Should we fine-tune a model or use RAG?
Use RAG when answers must reflect current, changing data. Fine-tune when you need a model to reliably adopt a specific format, tone, or narrow task at lower cost per call. Many enterprise systems combine both.
How much training data do we need to fine-tune?
Often less than teams expect. For format and tone adaptation, a few hundred to a few thousand high-quality examples can be enough. We help you assemble, clean, and label a dataset and measure whether more data is worth it.
Can the fine-tuned model run privately?
Yes. We fine-tune open models (e.g. Llama, Qwen) that you can host in your own cloud or on-prem, so the weights and your data never leave your environment.

Considering a custom model?

We will help you decide whether fine-tuning is worth it, and ship it if it is.

Discuss fine-tuning