AI capabilities / Custom model training
Custom model training for niches general models miss
Fine tuned models for vertical use, domain specific embeddings, and private deployment on hardware you own. Part of the same federation that runs your reasoning core and your data store, not a separate rented service.
When custom training beats a general model
Most organizations do not need custom model training. They need retrieval over their own content, running through a strong general model already living inside their federation. But for narrow vertical work where general models miss the vocabulary or the standards, fine tuning or domain specific embeddings earn their keep.
There are three real reasons to do custom model work. The domain has specialized vocabulary that general models handle poorly, such as a medical sub specialty, a niche compliance framework, or technical jargon unique to your field. The workload is high enough that querying a hosted model for every request becomes expensive or slow at your volume. Or the data cannot leave your environment for regulatory or contractual reasons, so a general model that lives outside your walls is not an option at all.
For most organizations, retrieval plus prompt design on a capable model inside the federation solves the problem outright. Custom training happens when there is a specific reason to do it, never as a default upsell.
The work that makes custom training succeed
Custom model work is a data project before it is a model project. The data needs to be high quality, large enough to generalize, and representative of the real inference workload. Every custom training engagement starts with a data audit: what you have, what its quality actually is, and what is missing before a single training run happens.
For fine tuning we use parameter efficient methods, LoRA and QLoRA, on open weight models such as Llama, Mistral, and Qwen that you can deploy yourself inside your federation. For domain specific embeddings we train on triplet loss over your own domain data. For deployment we run on Apple Silicon or on NVIDIA inference servers that live inside your environment, never a shared cloud endpoint.
Further reading: Joseph Anady on HuggingFace, the LoRA paper, the QLoRA paper, and MEGAMIND, the federated network this studio operates and trains on.
Stack
What the training layer is built from
Open weight starting point
Llama, Mistral, and Qwen, all deployable on hardware you control.
Parameter efficient methods
LoRA, QLoRA, and full fine tuning where the workload justifies it.
Domain specific vectors
BGE, E5, and custom triplet trained models tuned to your vocabulary.
Deployment inside your walls
Apple Silicon, NVIDIA, vLLM, and llama.cpp, sized to your latency budget.
Where this fits
Industries that benefit most
Specialty clinical support
Specialty specific clinical decision support running on premises rather than a shared model.
InsurancePolicy language fine tuning
Models tuned to policy language for high volume document questions and answers.
Tax and accountingCross state research
A state tax code embedding model built for cross state research work.
ResearchMEGAMIND
The federated network this studio operates on Apple Silicon, the proof behind this capability.
FAQ
Custom model training questions
Should every federation include custom training?
No. Most organizations get more value from retrieval over their own content paired with a strong general model already running inside the federation. We start with a feasibility read to confirm custom training is the right answer before committing to it.
Will the trained model run on my own hardware?
Yes. Models are sized to run on Apple Silicon or NVIDIA hardware inside your federation, chosen to fit your inference budget and latency needs.
How long does training take?
LoRA fine tuning on a seven billion parameter model runs hours to a day. Full fine tuning on a larger model runs days. Domain embedding training runs hours.
Who owns the model weights?
You do. You receive the trained weights, the training data, and the training code as part of the federation you own. Retrain, redeploy, or extend it on your own schedule.
How do you know the trained model is actually better?
Every engagement ships with an evaluation set and metrics the model is graded against before it is trusted with real work.
Begin
Should custom training be part of your federation?
The readiness assessment covers that question directly, along with everything else your federation would need to run.