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Domain language
Your products, part numbers, clauses and abbreviations, used correctly.
DEV-04 · TUNEcstmAI™ · Development · Fine-tuning & model training
Fine-tuning and model training on your own data.
We adapt open-weight models to your terminology, formats and tasks, measure the gain against an evaluation set, and deliver the weights to your storage, so a smaller model can do a specialized job as well as a large general one.
Fig. 01 · Integrating an electronics box, NASA Goddard
Fine-tuning is a tool, not a default. We reach for it when the evaluation set shows a gap that retrieval and prompting can't close: a strict output format, specialized vocabulary, a house style, or a speed target that calls for a smaller model.
Most of the work is data: choosing, cleaning and labeling examples, holding out a fair test set, and checking that the tuned model hasn't lost general ability. Training runs on your cstmAI hardware in cstmAI™ Studio, and the weights never leave your systems.
| Methods | Adapter fine-tuning (LoRA and similar); full fine-tuning; distillation |
|---|---|
| Data | Your examples, cleaned and labeled with your experts |
| Evaluation | Held-out test set plus regression checks on general skills |
| Hardware | Desk or Rack for adapters; Cluster for large full fine-tunes |
| Deliverables | Weights, a data manifest and an evaluation report |
What we train for
A gap you can measure, closed on your hardware.
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Format and style
Outputs that match a house template every time.
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Distillation
A small, fast model trained to match a large one on one task.
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Classifiers
Routing and labeling models trained on your own categories.
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Retrieval tuning
Embedding models adapted so search finds the right passages.
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Refresh cycles
A repeatable pipeline for retraining when your data changes.
Reel 02 · Lathe aisle seen from the catwalk, Paterson, NJ1994
How it's delivered
Four steps, each one signed off.
01
Find the gap
Score current models; confirm prompting and retrieval can't close it.
02
Build the data
Select, clean and label examples; hold out a test set.
03
Train & evaluate
Train on your GPUs and compare against the baseline.
04
Deploy & monitor
Serve the new weights and watch for drift.
FAQ
Questions we hear first.
How much data do we need?
Less than most people expect for adapter fine-tuning: often hundreds to a few thousand good examples for a narrow task. Quality matters more than volume, and we assess your data in discovery.
Who owns the fine-tuned model?
You do. Weights, data manifests and evaluation reports go to your storage. The base model's license still applies, and we review it with you.
Will it need retraining?
When your documents, products or rules change, yes. We set up monitoring that flags falling scores, and a pipeline that makes retraining routine.
Get a quote
Spec your system.
Tell us the models you want to run, how many people will use them and where the hardware should live. An engineer replies with a first configuration and the questions that decide the quote.