Prerequisites
- A Prem Studio account
- Basic familiarity with Python (for dataset adaptation scripts)
Adapt Nemotron Dataset
First, we need to convert the Nemotron Safety Guard Dataset into a JSONL format compatible with Prem Studio.Check out our tutorial on GitHub for a script to perform this conversion:
Nemotron Dataset Adaptation
View the tutorial on GitHub to convert Nemotron data to Prem-compatible JSONL.
Upload Dataset to Studio
Once you have your
JSONL file ready, upload it to Prem Studio.Follow the internal guide on uploading datasets:Upload a Dataset
Learn how to upload your JSONL dataset to Prem Studio.
Autosplit Dataset
After uploading, split your dataset into training and validation sets using the Autosplit feature.
How to Autosplit
Follow the guide to automatically split your dataset.
Create a Snapshot
Create a snapshot of your split dataset.The snapshot creates an immutable version of your data which is required for both fine-tuning and evaluation. This ensures that your results are reproducible and consistent across different runs.
Create a Snapshot
Learn how to freeze your dataset version for training.
Fine-Tuning and Experiments
Now that your data is ready, you can start the fine-tuning process.
Create a Fine-Tuning Job
Start by creating a fine-tuning job with your snapshot.Get Started with Fine-Tuning
Follow the guide to create your first fine-tuning job.
Configure Experiments
You can customize your training parameters using Experiments to get the best results.Experiment Settings
Learn how to configure and manage fine-tuning experiments.
Monitor Results
Once the training is complete, analyze the loss curves and metrics.View Results
Understand how to interpret your fine-tuning results.
Evaluation (BYOE)
Finally, evaluate your fine-tuned model using Bring Your Own Evaluation (BYOE).You can implement your own evaluation logic using our BYOE framework. Check out the tutorial below to learn how to set it up:
BYOE Tutorial
Learn how to implement and integrate your own safety evaluator script.