Private and fine-tuned models
Some data cannot go to a public AI service. We fine-tune open models on your examples, make them small enough to run on your own servers, and deploy them inside your network.
What you get
- LoRA and QLoRA fine-tuning on your examples
- 4-bit quantisation and merged adapters for cheaper, faster answers
- On-premise deployment, so data never leaves
How it works
- We prepare a training set from your own examples.
- We fine-tune with LoRA or QLoRA and evaluate against your real cases.
- We quantise and deploy on your hardware, with monitoring.
What you get
- LoRA and QLoRA fine-tuning on your examples
- 4-bit quantisation and merged adapters for cheaper, faster answers
- On-premise deployment, so data never leaves
A good fit when
- Regulation or contracts keep data on your premises
- A general model does not know your domain
- Per-call API costs are growing too fast
Where we have done this
7B vision-language adapters across document types, trained and served on-premise.
Talk to us about private and fine-tuned models
A 30-minute consultation: the problem, the data and what good would look like. We scope and quote after it.