Research · Model Training
Treat every training run like a controlled software change.
A run is useful only when its objective, data, configuration, lineage, evaluation, and rollback path can be explained. Training loss by itself does not establish model quality.
The exact artifact that may ship must be evaluated in the exact serving path that may run it.
Controlled Lifecycle
From a measured problem to a reversible release.
Define the objective
Name the narrow behavior to improve, the unacceptable regressions, and the metric that will decide the outcome.
Qualify inputs
Confirm base identity and license, data snapshot, tokenizer and template, target masking, hardware, and runtime compatibility.
Freeze the baseline
Capture current performance on a held-out suite before changing prompts, tools, retrieval, workflows, or weights.
Run a bounded canary
Use a deliberately small run to verify loading, batching, masking, optimization, checkpoints, and finite loss.
Train with lineage
Record the exact inputs, configuration, environment, seed, logs, checkpoints, and resulting artifact identity.
Evaluate and challenge
Compare candidate and baseline on frozen tasks, safety cases, regressions, uncertainty, efficiency, and failure recovery.
Qualify the exact artifact
Re-test conversion, merge, quantization, packaging, and the final serving runtime rather than assuming equivalence.
Promote or roll back
Release only when acceptance gates pass, limitations are documented, and the prior known-good artifact remains recoverable.
Evaluation
Measure behavior, not just optimization.
Task performance
Success, failure modes, partial completion, structured outputs, tool arguments, and recovery after errors.
Evidence & uncertainty
Grounding, source attribution, calibration, appropriate abstention, and separation of observed facts from inference.
Safety & authorization
Refusal boundaries, scope adherence, prompt-injection resistance, and regressions in security-sensitive behavior.
Operational fit
Latency, memory, throughput, context behavior, deterministic formatting, and reliability in the target runtime.
Artifact Identity
Related artifacts are not interchangeable evidence.
A base model, adapter, merged or fused model, floating-point export, quantized file, and packaged runtime are different evidence objects. Conversion and quantization can change behavior. Each candidate needs its own immutable identity and release record.
Evaluation of an adapter in one framework does not automatically qualify a merged or quantized artifact in another.
Current Evidence
A canary is evidence of wiring, not evidence of improvement.
The reviewed materials support reproducible data preparation, tokenizer and assistant-mask validation, a bounded finite-loss canary, and quarantine of an incompatible legacy adapter.
They do not yet support a public claim of completed full training, improved candidate quality, qualified converted or quantized output, or production promotion.
Choose the Intervention
Training is one option, not the default answer.
Before changing weights, compare prompt, policy, retrieval, tool, and workflow changes against parameter-efficient adaptation.