Lab and training
Lab and learning methods
Model improvement can start with clearer instructions, better retrieval or a corrected evaluation before training is justified. SAVRN Cloud's lab preview connects these choices to candidate review, making the distinct requirements of supervised tuning, adapters and reinforcement learning explicit.
SAVRN Cloud · Coming soon · Public preview · Browser-local simulation · No live compute or payments
Follow the improvement loop
Open Training to inspect a demonstration run and its configuration. Follow its output into Candidates, review related Evaluations, then inspect the promotion path into Deployments. Every metric, checkpoint description and generated candidate is synthetic.
The local walkthrough makes the release process visible. It does not change model weights, allocate training hardware or establish a quality improvement.
Choose the least complex useful change
A task may improve through prompt clarity, retrieval, tool design or an evaluation fix before training is needed. Supervised tuning uses approved examples; LoRA is an adaptation technique that can support such training. Reinforcement learning needs a validated reward process. Full parameter tuning and distributed training impose separate capacity and operational requirements.
These are distinct methods, not interchangeable checkboxes. A model fitting in memory for inference does not prove that its training workload fits.
Before connected services launch
Begin with one qualified recipe and a small approved dataset. Freeze the base model, tokenizer, training code, settings and splits. Capture actual metrics and checkpoint identity, then compare against a held-out baseline. Promotion requires an authorized decision and a controller that observes the new serving identity. Retain rollback. Public support for one recipe should not imply arbitrary-model training or all learning methods.
Build toward the work that matters.
Tell SAVRN what your institution needs to run, who reviews the results and where its data must stay. That workload defines the next service to qualify.
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