> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo-helix/_mcp/server.

# Fine-Tuning Tutorials

Use the tutorials in this section to gain a deeper understanding of how the NVIDIA NeMo Customizer microservice enables fine-tuning tasks.

> **Tip**
>
> Tutorials are organized by complexity and typically build on one another. The tutorials connect to the deployment in your current CLI context. Refer to the [Setup guide](/documentation/get-started) for installation and setup, and to [Connect with CLI Contexts](/documentation/reference/cli-reference/connect-to-deployments) to connect to another deployment.

---

## Getting Started

#### [Understanding Model Entities and Adapters](/documentation/customizer-reference/tutorials/understanding-models-and-training)

Learn the fundamentals of how NeMo Customizer works with Model Entities and Adapters, and how to choose the right approach for your project.

model-entities adapters training-types

## Dataset Preparation

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

Learn how to format datasets for different model types.

datasets chat-models completion-models

#### [GRPO Environment Packages](/documentation/customizer-reference/tutorials/grpo-environment-packages)

Package `native-v1`, `wheels-v1`, and `adapter-wheels-v1` environment FileSets for GRPO.

grpo nemo-gym filesets

## Customization Jobs

#### [Fine-Tune a Model with Custom Data Using LoRA](/documentation/customizer-reference/tutorials/lora-customization-job)

Learn how to perform supervised fine-tuning with LoRA adapters using custom data.

nemo-customizer

#### [Fine-Tune a Model with Custom Data Processing All Weights](/documentation/customizer-reference/tutorials/sft-customization-job)

Learn how to perform supervised fine-tuning using custom data by modifying all training parameters.

nemo-customizer

#### [Align a Model with Direct Preference Optimization](/documentation/customizer-reference/tutorials/dpo-customization-job)

Learn how to run a DPO customization job with preference pairs on the RL backend.

nemo-customizer dpo

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

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

nemo-customizer knowledge-distillation

#### [Fine-Tune Nemotron 3 Embed for Domain Retrieval](/documentation/customizer-reference/tutorials/embedding-customization-job)

Mine hard negatives from NVIDIA's published NVDocs dataset, fine-tune Nemotron 3 Embed 1B, then deploy Retriever NIM and compare nDCG and Recall against the base model.

embedding-models bi-encoder nemo-customizer

#### [Train on SPECTER or Triplet JSONL](/documentation/customizer-reference/tutorials/embedding-triplet-dataset)

Register an existing triplet dataset as a fileset and train on it instead of mined NVDocs data.

embedding-models datasets nemo-customizer

## Monitoring & Optimization

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

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

nemo-customizer mlflow wandb

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

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

nemo-customizer wandb sequence-packing

## Docs

- [Understanding NeMo Customizer: Models, Training, and Resources](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/understanding-models-and-training.md): 
- [Format Training Dataset](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/format-training-dataset.md): 
- [GRPO Environment Packages](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/grpo-environment-packages.md): Package native-v1, wheels-v1, and adapter-wheels-v1 environment FileSets for GRPO.
- [Import and Fine-Tune Private Hugging Face Models](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/import-hugging-face-models.md): 
- [Full SFT Customization](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/sft-customization-job.md): 
- [DPO Customization](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/dpo-customization-job.md): 
- [LoRA Model Customization](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/lora-customization-job.md): 
- [Knowledge Distillation Customization](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/distillation-customization-job.md): 
- [Embedding Model Customization](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/embedding-customization-job.md): 
- [Train on SPECTER or Triplet JSONL](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/embedding-triplet-dataset.md): 
- [Checking Your Customization Job Metrics](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/metrics.md): 
- [Optimize for Tokens/GPU Throughput](https://docs.nvidia.com/nemo-helix/v0.8.0/documentation/customizer-reference/tutorials/optimize-throughput.md):