End-to-End Fine-tuning
Use paired datasets and a stable eval baseline to launch fine-tuning or distillation safely.
Use this guide when prompt changes are no longer enough and you need to improve the model itself.
What you'll have when you finish
- one training job launched against paired training and eval datasets
- a clear training objective tied to the eval baseline
- a checklist for deciding whether the result is worth promoting
Before you start
- a stable quality baseline from an AutoEval
- both a training dataset and an eval dataset from Datasets
Step 1: decide whether this is a training problem
Training is worth it when:
- the baseline model consistently fails on the same task pattern
- the eval dataset is representative and stable
- the improvement target matters enough to justify a new model artifact
If the issue is still mostly prompt quality or dataset quality, fix those first.
Step 2: choose the model improvement path
Use fine-tuning when the base model is close but not good enough.
Use distillation when the teacher model already performs well and your bigger problem is cost, latency, or serving footprint.
Step 3: launch the training job with paired datasets
The current self-serve flow expects:
- one training dataset
- one eval dataset
- one eval definition and version
- one base model
Training jobs should be anchored to the same eval rubric you used to decide that the baseline was not good enough.
Step 4: monitor the run, not just the final status
Watch the training job detail page for:
- queued vs running vs completed vs failed
- current step vs total steps
- current loss
- checkpoint evals and score distribution
- final model reference and weights
Checkpoint evals matter because they tell you whether the model is improving before the job finishes.
Step 5: decide if the result is good enough to promote
A completed training job is not automatically a production-ready model.
Before promotion, confirm:
- the training job finished successfully
- the model beats or matches the baseline on the eval you trust
- the latency and cost tradeoffs still make sense
Verify it worked
You should now have:
- one completed training job or one active job with a clear monitoring path
- one model reference to evaluate for promotion