Manage Customization Jobs
Use customization jobs to fine-tune a model using a dataset and hyperparameters.
How It Works
A customization job references a Model Entity that contains the base model checkpoint, and is submitted to one of two backends — automodel (default, multi-GPU) or unsloth (single-GPU, with optional quantized loading for LoRA). The job then runs on the platform’s GPU cluster. When training completes:
- LoRA jobs: Create an Adapter attached to the original Model Entity. Adapters can be auto-deployed to NIMs.
- Full fine-tuning jobs: Create a new Model Entity with the customized weights, linked to the base model.
This design keeps adapters organized with their parent models and simplifies deployment workflows.
Submission is backend-specific (you submit to the automodel or unsloth backend), but every job runs on the shared platform Jobs service. As a result, you poll status, list, and cancel jobs through that service the same way regardless of which backend you used.
Prerequisites
Before you can customize a model using a customization job, make sure that you have prepared and uploaded a dataset to the dataset repository.
Task Guides
Perform common customization job tasks.
The task guides connect to the deployment in your current CLI context. After the standard Setup flow, that is the local platform at http://localhost:8080. To connect to another deployment, refer to Connect with CLI Contexts.
References
Refer to the following pages for more information on customization jobs.