Phase 6 · Modern LLM engineering · Adapting models
Fine-tune an open model
Run a real fine-tune and evaluate the result.
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Why it matters
Every earlier lesson in this phase taught one piece. A real fine-tuning project is mostly about the parts around the training loop: deciding what "better" means, building trustworthy data, and measuring honestly. Teams that skip those steps ship models that look good on their training data and disappoint in use. This lesson runs the whole workflow, in miniature, in your browser.
What you'll be able to do
- Run a fine-tuning project end to end: metric, data, baseline, training, checkpoint choice, test
- Curate training data by filtering model outputs with an automatic checker
- Use a validation set to pick a checkpoint, and a separate test set to report the result
- Adapt the same workflow to a real open model with LoRA on a GPU
Before this lesson
Next in this phase: Distillation