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NeMo Studio Custom Models

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Use Models > Fine-tune in the NeMo Studio workspace sidebar to open the Custom Models list, start Customizer jobs, and track their progress for the selected workspace.

Custom Models is enabled by default. An administrator can disable it by setting studio.feature_flags.customizer_enabled: false in the platform configuration and restarting Studio, which removes the Custom Models navigation entry and hides Customizer jobs from the general Jobs list.

For customization concepts, supported models, and the underlying CLI and API workflow, see Customization Concepts.

Start a Customization Job

Select Customize a Model from the Custom Models list, or open a base model’s details panel and select Customize this Model. Both open Fine-tune a Model, a single form with the following sections, shown as they apply to your selections:

SectionWhat you configure
Training BackendAutomodel (multi-GPU), Unsloth (single-GPU, 4-bit quantized), or RL.
ModelBase model, output model name, and description.
Training MethodFor Automodel and Unsloth: fine-tuning type (LoRA, LoRA (Merged), or Full Weights), plus, for Automodel, training type (SFT or Distillation, with a teacher model field for distillation). For RL: DPO or GRPO.
Reward EnvironmentShown for GRPO jobs. Selects the reward environment used during training.
DatasetThe training fileset, selected from filesets available in the workspace.
ParametersGeneral hyperparameters, or GRPO-specific parameters when the training method is GRPO.
LoRA ParametersShown when the training method uses a LoRA or LoRA-merged finetuning type.
DPO ParametersShown for RL jobs using DPO.
Compute ResourcesGPU and node allocation for the job.

Several sections include an advanced JSON field for power-user overrides — for example raw parallelism or backend-specific keyword arguments — alongside the structured form fields. Use these only when a setting isn’t exposed elsewhere in the form.

Select Start Fine-Tuning to create the job. Studio confirms with a toast and navigates to the new job’s detail page.

Model eligibility for fine-tuning depends on the selected base model; Customize this Model is disabled for models that have no fileset to fine-tune from.

Clone an Existing Job

From a job’s detail page, open the actions menu (⋯) and select Clone to open Fine-tune a Model pre-filled with that job’s settings. Adjust any fields before submitting to start a new job.

Custom Models List

The Custom Models list shows fine-tuned models created in the current workspace. Select a row to open the model details side panel, which includes Model Details and Chat Playground tabs, a Customize this Model action, an Evaluate this Model action, and a View Intake Traces link when intake is enabled.

Customization Job Details

Select a job to open its detail page, which shows the job name, status badge, and metadata such as training type, base model, and creation date.

TabShows
OverviewTraining progress, run configuration, and the source dataset’s fileset details.
LogsJob execution logs.
Chat with your ModelA chat playground against the resulting model, shown once the job completes.

The Overview tab’s training progress panel differs by training method:

Training methodPanelShows
SFT, distillation, DPOTraining LossTrain and validation loss per step, plus loss and progress stat tiles.
GRPORewardTraining and validation reward per step (loss isn’t meaningful for GRPO’s policy-gradient objective), plus reward and progress stat tiles.
GRPOTraining HealthCollapsed by default. Diagnostic charts from NeMo RL, such as KL divergence and clip fraction, that can flag a run diverging before reward does.

Below the training panel, Run Configuration summarizes the customization ID, output model, base model, and other run metadata, with a View Job Configuration action that opens the full job spec.

From the detail page header, select Evaluate to start an evaluation against the resulting model once the job is launchable, or open the actions menu (⋯) for Clone and, while the job is active or pending, Cancel Job.