Small adapters. Serious specialisation.
LoRA trains a thin layer on top of an open model instead of the whole thing, so you get a specialised model in days, on modest hardware, with weights you fully own.
In plain words.
Low-Rank Adaptation freezes the base model and trains small adapter matrices that plug into it. Adapters are megabytes, not gigabytes, so you can keep one per task, client or language and swap them at runtime. QLoRA brings the same idea to even smaller GPUs.
One model, many tasks
Separate adapters for support, sales and internal search on one base model.
Sinhala and Tamil
Adapt open models to local languages and mixed-language chat.
Brand imagery
Image-model LoRAs that render your products and visual style consistently.
On-premise AI
Specialised models small enough to run on your own hardware.
How we deliver it.
Discover
We map the process, the data and the numbers you want to move.
Prototype
A working pilot on your real examples, usually within weeks.
Build
Production integration, guardrails, logging and a Nyoxa security review.
Run
Monitoring, retraining and monthly improvements under a support plan.
What you get.
- LoRA or QLoRA adapter weights you own
- A benchmark against the base model
- A serving setup with adapter switching
- Documentation to retrain on new data
Fair questions.
LoRA or full fine-tuning?
For most business tasks LoRA reaches most of the quality of full fine-tuning at a fraction of the compute. We recommend full fine-tuning only when evaluation shows it is worth it.
Which base models?
Open-weight families such as Llama, Mistral, Qwen and Gemma for text, and diffusion models for images. We choose by licence, language support and size.
Can it run offline?
Yes. Quantised models with LoRA adapters run on a single GPU server inside your network.
Let's scope your pilot.
Tell us where LoRA adapters could save your team time. We will reply within one working day with honest next steps.