Train private models
Fine-tune models on your own data, inside your own environment. You retain full ownership of the weights that come out, and they stay inside the boundary they were trained in.
A private model answering live
The real interface: a private Blue Mesh model, Privy-V3, answering on a pipeline's model node, each reply with its token count and latency (1.4x speed). Weights from an Oasis run are served from that same node.
From your examples to a model you own
Your data stays where it is
Training reads the examples you already hold, inside your environment. No data is sent to Blue Mesh to run the job; the run happens on your capacity. Your team supplies the examples; the run is set up with you and operated on your infrastructure, by your people or ours as you choose.
- Examples you already hold
- Inside your own boundary
A base model is chosen for the job
A smaller model that fits the task is adapted rather than a general-purpose one, which is what keeps the running cost down later.
- Sized to the task
- Runs on your capacity
The model learns your way of working
The model picks up how your organization writes and decides: its formats, its vocabulary, what a complete answer looks like. Facts that change stay in retrieval.
- Behavior, not today's facts
- Your formats and vocabulary
Checked before it is used
The tuned model is run against examples held back from training, so the decision to deploy rests on results your team can read rather than on a promise.
- Held-back examples
- Results your team reads
Served from your own infrastructure
The weights go onto a model node in your platform and answer from there, under the access controls you already govern.
- Your model node
- Your access controls
Everything the run produces stays yours
The trained model is yours. It is not licensed back to you and it does not leave the boundary it was trained in.
Your examples stay where they were, unchanged. Training reads them; it does not take them.
What the model got right and wrong on the held-back set, kept with the run so the deployment decision has a record behind it.
What was trained, from what, when and by whom, written in your environment under your retention rules.
Ownership of the tuned weights is subject to the licence of the base model you start from, and we tell you which base models carry which terms before a run starts.
Fine-tuning is not the answer to every question
Fine-tuning teaches a model your domain, your formats, and your way of doing things. It does not teach it today's facts, and choosing it for a facts problem is the wrong starting point.
Start with retrieval instead
If what you need is answers grounded in current documents, Enterprise RAG gets you there faster and stays current as the documents change. Fine-tuning a model on facts means retraining it every time they move.
Fine-tune for behavior
Tone, structure, domain vocabulary, and the shape of a good answer in your organization are the things training is the better tool for.
You need compute in your environment
Training runs where your data lives, which means GPU capacity on your side of the boundary. If you do not have it, BM in a Box ships the hardware pre-configured.
Smaller models, on your own capacity
A tuned private model is usually much smaller than a general-purpose one for the same task, and it runs against your own capacity rather than metered public API tokens.
- BM in a Box The GPU capacity named in the precondition above, shipped pre-configured on NVIDIA DGX hardware.
- Enterprise RAG The retrieval route named in the boundary above: answers from your own content, with the source named in the reply.
- Banking and finance Ledger Lens, where the models learn a firm's own transaction categories from the firm's own statements.
Bring the data you would fine-tune on
In one session we will look at what you have, say plainly whether training or retrieval is the right tool for it, and show you what the trained model would be built from.