Model Training
Task-specific supervised fine-tuning (SFT), domain adaptation, instruction curation, and preference alignment pipelines designed for enterprise production workloads.
What We Train
We develop supervised fine-tuning datasets, parameter-efficient adaptations (LoRA/QLoRA), full-parameter checkpoints, and direct alignment workflows (DPO/RLHF) tailored to client domain schemas, API syntax, and task requirements.
Preventing Catastrophic Forgetting
Fine-tuning models on narrow domain records often degrades their general reasoning or instruction-following capabilities. We build paired regression suites that test both target task mastery and general capabilities after every training epoch.
| Metric | Phase | Description |
|---|---|---|
| Target Task Accuracy | VALIDATION | Task completion rate and exact schema conformity on held-out domain test sets. |
| Regression Retention Delta | CONTROL | Performance preservation on baseline general reasoning tasks across training checkpoints. |
| Validation Loss Convergence | EPOCH | Evaluation loss curve tracking to prevent overfitting on narrow training distributions. |
| Capability Benchmark | Base Checkpoint | Fine-Tuned Checkpoint | Delta |
|---|---|---|---|
| Domain SQL Query Synthesis | 54.2% | 89.4% | +35.2% |
| Complex Schema Adherence | 68.0% | 96.8% | +28.8% |
| General Instruction Retention | 89.1% | 88.8% | -0.3% (Preserved) |
Fine-Tune for Your Exact Workload
Schedule an engineering consultation to discuss supervised fine-tuning and task adaptation under mutual NDA.