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

# Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

> Use Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 with NeMo AutoModel for multimodal fine-tuning, including checkpoints, runnable recipes, setup, and reference details.

[Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) is NVIDIA's
omnimodal reasoning model. It pairs a NemotronH (hybrid Mamba-2 + Attention) MoE
language backbone with a RADIO v2.5-H vision encoder and a Parakeet (FastConformer)
sound encoder, supporting interleaved text, image, and audio inputs.

Set up NeMo AutoModel with the [latest container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo-automodel) or follow the [installation instructions](/get-started/installation).

## Fine-Tune Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

From the repository root, run:

```bash
uv run automodel examples/vlm_finetune/nemotron_omni/nemotron_omni_cord_v2.yaml --nproc-per-node 8
```

For a full walkthrough - dataset preparation, SFT vs. LoRA configs, and
post-training inference - see the
[Nemotron-Omni guide](/recipes-e2e-examples/nemotron-omni).

## Choose a Workflow

| Goal                                              | Start Here                                                                                                                                                                           |
| ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Full SFT - receipt parsing                        | Use [nemotron\_omni\_cord\_v2.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/nemotron_omni/nemotron_omni_cord_v2.yaml). Dataset: CORD-v2.            |
| Low-rank adaptation (LoRA) PEFT - receipt parsing | Use [nemotron\_omni\_cord\_v2\_peft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/nemotron_omni/nemotron_omni_cord_v2_peft.yaml). Dataset: CORD-v2. |

## Model Reference

### Model Architecture

| Property                  | Value                                   |
| ------------------------- | --------------------------------------- |
| Task                      | Omnimodal (Text/Image/Audio)            |
| Architecture              | `NemotronH_Nano_Omni_Reasoning_V3`      |
| Parameters                | 30B total / 3B active                   |
| Hugging Face Organization | [nvidia](https://huggingface.co/nvidia) |

### Available Models

* **Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16**: 30B total, 3B activated (MoE)

## Related Resources

* [VLM Fine-Tuning Guide](/recipes-e2e-examples/gemma-3-3n)