Off-the-shelf models miss your terminology, formats and edge cases, and prompting alone only goes so far.
11 / LLM Fine Tuning
LLM Fine Tuning
Tailor a model to your domain so it answers the way your business needs.
Models adapted to your data, vocabulary and tasks for better accuracy at lower cost.
We first check whether prompting or retrieval already solves the task, because fine tuning is only worth it when they fall short. If it is, we build the training set from your examples, train a small number of variants, and compare them head to head on a frozen test set.
What the build includes
A complete production workflow.
Cleaned training dataset
Instruction and answer pairs drawn from your records, deduplicated, anonymised where needed and reviewed by a subject expert.
Fine-tuned model weights
An adapted model with its training configuration, so the run can be repeated.
Benchmark report
Side-by-side results against the base model and a hosted baseline on your own test set.
Serving setup
An inference endpoint sized for your traffic, with latency and cost per request measured.
Where it applies
Typical llm fine tuning work.
- →Classifying tickets with company-specific labels
- →Writing in a fixed clinical or legal format
- →Running a small model on private infrastructure
- →Replacing a costly large model for a narrow task
How a project runs
Four steps, no surprises.
- 01
Discovery call
A short call about the goal and the first workflow or feature worth building.
- 02
Written scope
What is built, which tools it touches, what a person approves, and the timeline, agreed before work starts.
- 03
Build and review
Short iterations connected to your tools, reviewed with your team before anything ships.
- 04
Launch and handoff
Go-live with documentation and an owner on your side. You own what we build.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
PyTorchHugging FaceLLaMAQwenPython
Industries
Expected outcomes
- →A model that knows your terminology
- →Proof it beats the baseline, or an honest answer that it does not
- →Lower running cost for repeat tasks
- →Weights you control
- →A repeatable path to retrain
Common questions
Before we scope it.
Do we really need fine tuning?
Often not. We test a strong prompt and retrieval setup first and tell you plainly if that is enough. Fine tuning is recommended only when the numbers justify it.
How much data is required?
Usually a few hundred to a few thousand good examples for a narrow task. Quality matters more than volume, and we help you assemble and clean them.
Who owns the resulting model?
You do, subject to the licence of the open-weight base model, which we explain before we start. The adapters and training code are handed over.
Keep exploring
Related services and technologies.
Related services
Free Audit
Get my free Audit ↗Know what to build first.
We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.