---
title: "About Fine-Tuning"
description: ""
---

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

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).
1. Format a compatible [dataset](/documentation/customizer-reference/tutorials/format-training-dataset).
1. [Create a customization job](/documentation/customizer-reference/manage-customization-jobs) referencing the Model Entity.
1. Monitor the job until it completes.
1. 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.
1. Move on to [Evaluate the output model](/documentation/evaluate-models).

---

## Container Images

Fine-tuning jobs run in container images published to NVIDIA NGC. For NeMo Platform `0.2.0`, the fine-tuning images are:

| Image | Purpose |
|-------|---------|
| `nvcr.io/nvidia/nemo-platform/nmp-customizer-tasks:0.2.0` | Shared CPU task steps (file I/O, model entity, model spec) for all customization backends |
| `nvcr.io/nvidia/nemo-platform/nmp-automodel-training:0.2.0` | Automodel GPU training step |
| `nvcr.io/nvidia/nemo-platform/nmp-unsloth-training:0.2.0` | 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:0.2.0
docker pull nvcr.io/nvidia/nemo-platform/nmp-automodel-training:0.2.0
docker pull nvcr.io/nvidia/nemo-platform/nmp-unsloth-training:0.2.0
```

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.

<Cards>

<Card title="Llama Models" href="/documentation/customizer-reference/models/llama">

View the available Llama models in the model catalog.

</Card>
<Card title="Llama Nemotron Models" href="/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.

</Card>
<Card title="Phi Models" href="/documentation/customizer-reference/models/phi">

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

</Card>
<Card title="GPT-OSS Models" href="/documentation/customizer-reference/models/gpt-oss">

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

</Card>
<Card title="Embedding Models" href="/documentation/customizer-reference/models/embedding">

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

</Card>

</Cards>

## Task Guides

Perform common fine-tuning tasks.

<Cards>

<Card title="Manage Customization Jobs" href="/documentation/customizer-reference/manage-customization-jobs">

Create, list, view, and cancel customization jobs.

</Card>
<Card title="Manage Model Entities" href="/documentation/customizer-reference/manage-model-entities/overview">

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

</Card>
<Card title="Manage Datasets" href="/documentation/get-started/core-concepts/manage-files">

Upload and manage datasets for training.

</Card>

</Cards>

---

## Tutorials

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

<Cards>

<Card title="Format Training Datasets" href="/documentation/customizer-reference/tutorials/format-training-dataset">

Learn how to format datasets for different model types.

datasets chat-models completion-models

</Card>
<Card title="Start a LoRA Customization Job" href="tutorials/lora-customization-job.ipynb">

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

nemo-customizer

</Card>
<Card title="Start a Full SFT Customization Job" href="tutorials/sft-customization-job.ipynb">

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

nemo-customizer

</Card>
<Card title="Distill a Model with Knowledge Distillation" href="tutorials/distillation-customization-job.ipynb">

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

nemo-customizer knowledge-distillation

</Card>
<Card title="Check Customization Job Metrics" href="/documentation/customizer-reference/tutorials/metrics">

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

nemo-customizer mlflow wandb

</Card>
<Card title="Optimize Tokens per GPU" href="tutorials/optimize-throughput.ipynb">

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

nemo-customizer wandb sequence-packing

</Card>

</Cards>

---

## References

<Cards>

<Card title="Hyperparameters" href="/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.

</Card>
<Card title="Customizer API" href="/documentation/reference/api-reference">

View the OpenAPI specification for Customizer.

</Card>
<Card title="Troubleshoot Failed Jobs" href="/documentation/reference/troubleshooting/customizer">

View troubleshooting tips for failed jobs.

</Card>

</Cards>
