> This page is for version 0.5.
> For other versions, use one of these documentation indexes:
> - Latest (default): https://docs.nvidia.com/nemo-helix/latest/llms.txt
> - 0.9: https://docs.nvidia.com/nemo-helix/v0.9/llms.txt
> - 0.8: https://docs.nvidia.com/nemo-helix/v0.8/llms.txt
> - 0.7: https://docs.nvidia.com/nemo-helix/v0.7/llms.txt
> - 0.6: https://docs.nvidia.com/nemo-helix/v0.6/llms.txt
> - 0.5: https://docs.nvidia.com/nemo-helix/v0.5/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo-helix/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 reference `NMP_BASE_URL`, which is the base URL of your NeMo Helix deployment. Refer to the [Setup guide](/nemo-helix/v0.5/documentation/get-started) for installation, setup, and platform URL guidance.

---

## Getting Started

#### [Understanding Model Entities and Adapters](/nemo-helix/v0.5/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](/nemo-helix/v0.5/documentation/customizer-reference/tutorials/format-training-dataset)

Learn how to format datasets for different model types.

datasets chat-models completion-models

#### [GRPO Environment Packages](/nemo-helix/v0.5/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](/nemo-helix/v0.5/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](/nemo-helix/v0.5/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](/nemo-helix/v0.5/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](/nemo-helix/v0.5/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 an Embedding Model With Positive and Negative Samples Using LoRA](/nemo-helix/v0.5/documentation/customizer-reference/tutorials/embedding-customization-job)

Learn how to fine-tune embedding models using LoRA merged training for improved question-answering and retrieval tasks.

embedding-models lora-merged nemo-customizer

## Monitoring & Optimization

#### [Check Customization Job Metrics](/nemo-helix/v0.5/documentation/customizer-reference/tutorials/metrics)

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

nemo-customizer mlflow wandb

#### [Optimize Tokens per GPU](/nemo-helix/v0.5/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