Checkpoint

What it means

It allows resuming training or deploying a specific version. In training workflows, checkpoint often shapes model adaptation.

How it works

Training adapts models through fine-tuning or preference optimization. It uses curated datasets and evaluation loops.

Why it matters

Training methods tailor models to your domain and use case.

Common use cases

• Adapting a base model to your domain or style.
• Improving instruction following for specific tasks.
• Reducing errors with better training data.

Example

Load the best checkpoint for evaluation.

Pitfalls and tips

Low-quality data can degrade performance. Keep datasets clean, representative, and well-labeled.

In BoltAI

In BoltAI, this is referenced when discussing model customization.