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# About Fine-Tuning

<a id="ft-about" />

Learn how to fine-tune models by making requests to NVIDIA NeMo Customizer through the API. Fine-tuned models you have created can be deployed using NVIDIA NIMs.

## Fine-Tuning Workflow

<a id="ft-workflow" />

At a high level, the fine-tuning workflow consists of the following steps:

1. [Create a Model Entity](/documentation/customizer-reference/manage-model-entities/overview) pointing to your base model checkpoint (stored as a FileSet).
2. Format a compatible [dataset](/documentation/customizer-reference/tutorials/format-training-dataset).
3. [Create a customization job](/documentation/customizer-reference/manage-customization-jobs) referencing the Model Entity.
4. Monitor the job until it completes.
5. The customization job automatically creates either:

* **LoRA jobs**: An adapter attached to the original Model Entity
* **Full fine-tuning jobs**: A new Model Entity with the customized weights

1. [Deploy the model](/documentation/models-and-inference) using the Deployment Management Service.
2. Move on to [Evaluate the output model](/documentation/evaluate-models).

---

## Container Images

Fine-tuning jobs run in container images published to NVIDIA NGC. Use the image tag that matches your NeMo Platform release:

| Image                                                             | Purpose                                                                                   |
| ----------------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `nvcr.io/nvidia/nemo-platform/nmp-customizer-tasks:<image-tag>`   | Shared CPU task steps (file I/O, model entity, model spec) for all customization backends |
| `nvcr.io/nvidia/nemo-platform/nmp-automodel-training:<image-tag>` | Automodel GPU training step                                                               |
| `nvcr.io/nvidia/nemo-platform/nmp-unsloth-training:<image-tag>`   | Unsloth GPU training step                                                                 |

These public images can be pulled directly from `nvcr.io`:

```bash
docker pull nvcr.io/nvidia/nemo-platform/nmp-customizer-tasks:<image-tag>
docker pull nvcr.io/nvidia/nemo-platform/nmp-automodel-training:<image-tag>
docker pull nvcr.io/nvidia/nemo-platform/nmp-unsloth-training:<image-tag>
```

Most users do not need to pull these images manually. NeMo Platform resolves them from the configured platform image registry and tag. Pull or mirror them when you are operating a self-managed deployment, preloading an air-gapped environment, or validating registry access. If your environment requires authenticated registry pulls, authenticate to `nvcr.io` with an NGC API key before pulling or mirroring the images.

---

## Model Catalog

Explore the model families and sizes supported by NVIDIA NeMo Customizer.

#### [Llama Models](/documentation/customizer-reference/models/llama)

View the available Llama models in the model catalog.

#### [Llama Nemotron Models](/documentation/customizer-reference/models/llama-nemotron)

View the available Llama Nemotron models from NVIDIA, including Nano and Super variants for efficient and advanced instruction tuning.

#### [Phi Models](/documentation/customizer-reference/models/phi)

View the available Phi models from Microsoft, designed for strong reasoning capabilities with efficient deployment.

#### [GPT-OSS Models](/documentation/customizer-reference/models/gpt-oss)

View the available GPT-OSS models supported for Full SFT and LoRA customization.

#### [Embedding Models](/documentation/customizer-reference/models/embedding)

View the available embedding models for question-answering and retrieval tasks.

## Task Guides

Perform common fine-tuning tasks.

#### [Manage Customization Jobs](/documentation/customizer-reference/manage-customization-jobs)

Create, list, view, and cancel customization jobs.

#### [Manage Model Entities](/documentation/customizer-reference/manage-model-entities/overview)

Create FileSets and Model Entities to prepare base models for customization.

#### [Manage Datasets](/documentation/get-started/core-concepts/manage-files)

Upload and manage datasets for training.

---

## Tutorials

Follow these tutorials to learn how to accomplish common fine-tuning tasks.

#### [Format Training Datasets](/documentation/customizer-reference/tutorials/format-training-dataset)

Learn how to format datasets for different model types.

<small>
  datasets

   

  chat-models

   

  completion-models
</small>

#### [Start a LoRA Customization Job](/documentation/customizer-reference/tutorials/lora-customization-job)

Learn how to start a LoRA customization job using a custom dataset.

<small>
  nemo-customizer
</small>

#### [Start a Full SFT Customization Job](/documentation/customizer-reference/tutorials/sft-customization-job)

Learn how to start a SFT customization job using a custom dataset.

<small>
  nemo-customizer
</small>

#### [Distill a Model with Knowledge Distillation](/documentation/customizer-reference/tutorials/distillation-customization-job)

Learn how to compress a larger teacher model into a smaller student model.

<small>
  nemo-customizer

   

  knowledge-distillation
</small>

#### [Check Customization Job Metrics](/documentation/customizer-reference/tutorials/metrics)

Learn how to check job metrics using MLFlow or Weights & Biases.

<small>
  nemo-customizer

   

  mlflow

   

  wandb
</small>

#### [Optimize Tokens per GPU](/documentation/customizer-reference/tutorials/optimize-throughput)

Learn how to optimize the token-per-GPU throughput for a LoRA optimization job.

<small>
  nemo-customizer

   

  wandb

   

  sequence-packing
</small>

---

## References

#### [Hyperparameters](/documentation/customizer-reference/manage-customization-jobs/training-configuration)

View the available hyperparameters and their valid options that you can set when creating a customization job.

#### [Customizer API](/documentation/reference/api-reference)

View the OpenAPI specification for Customizer.

#### [Troubleshoot Failed Jobs](/documentation/reference/troubleshooting/customizer)

View troubleshooting tips for failed jobs.