Tuning & Scaling
With your data configured, the remaining YAML blocks control how the foundation model trains and how it uses your hardware. Basic Configuration introduces a minimal training_params block with batch limits for a smoke test — the guides below expand on that.
In this section
| Guide | Description |
|---|---|
| Training Behavior | Control optimization, sampling, early stopping, and more as you refine your model |
| Scaling & Memory | Adjust distribution, batch sizing, and memory when defaults under- or over-utilize your infrastructure |