> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo-platform/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo-platform/_mcp/server. # NeMo Studio Custom Models > Fine-tune models from NeMo Studio, and track customization jobs. 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](/documentation/customizer-reference/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: | Section | What you configure | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Training Backend | **Automodel** (multi-GPU), **Unsloth** (single-GPU, 4-bit quantized), or **RL**. | | Model | Base model, output model name, and description. | | Training Method | For 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 Environment | Shown for GRPO jobs. Selects the reward environment used during training. | | Dataset | The training fileset, selected from filesets available in the workspace. | | Parameters | General hyperparameters, or GRPO-specific parameters when the training method is GRPO. | | LoRA Parameters | Shown when the training method uses a LoRA or LoRA-merged finetuning type. | | DPO Parameters | Shown for RL jobs using DPO. | | Compute Resources | GPU 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. | Tab | Shows | | -------------------- | ------------------------------------------------------------------------------- | | Overview | Training progress, run configuration, and the source dataset's fileset details. | | Logs | Job execution logs. | | Chat with your Model | A chat playground against the resulting model, shown once the job completes. | The Overview tab's training progress panel differs by training method: | Training method | Panel | Shows | | ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | SFT, distillation, DPO | Training Loss | Train and validation loss per step, plus loss and progress stat tiles. | | GRPO | Reward | Training and validation reward per step (loss isn't meaningful for GRPO's policy-gradient objective), plus reward and progress stat tiles. | | GRPO | Training Health | Collapsed 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**. ## Related Topics * [Customization Concepts](/documentation/customizer-reference/customization-concepts) * [Create a Customization Job](/documentation/customizer-reference/manage-customization-jobs/create-a-customization-job) * [Customization Job Reference](/documentation/customizer-reference/manage-customization-jobs/customization-job-reference) * [Experiments](/documentation/evaluate-models/experiments)