> 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.

# Qwen3-Omni-30B-A3B-Instruct

> Use Qwen3-Omni-30B-A3B-Instruct with NeMo AutoModel for multimodal fine-tuning, with documented checkpoints, runnable recipes, setup, and model reference details.

[Qwen3-Omni](https://qwenlm.github.io/blog/qwen3/) is Alibaba Cloud's omnimodal model supporting text, image, audio, and video inputs in a single unified architecture with a MoE language backbone. NeMo AutoModel supports supervised ASR fine-tuning of its audio-capable Thinker stack.

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 Qwen3-Omni-30B-A3B-Instruct

From the repository root, run:

```bash
uv run automodel --nproc-per-node=8 examples/vlm_finetune/qwen3/qwen3_omni_moe_30b_te_deepep.yaml
```

## Choose a Workflow

| Goal                                                           | Start Here                                                                                                                                                                           |
| -------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Supervised fine-tuning (SFT) - Qwen3-Omni 30B with TE + DeepEP | Use [qwen3\_omni\_moe\_30b\_te\_deepep.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3/qwen3_omni_moe_30b_te_deepep.yaml). Dataset: MedPix-VQA. |
| ASR SFT - Qwen3-Omni 30B-A3B                                   | Use [ami\_sft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/audio_finetune/qwen3_omni_asr/ami_sft.yaml). Dataset: AMI.                                           |
| ASR SFT - Qwen3-Omni 30B-A3B                                   | Use [multi\_en\_sft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/audio_finetune/qwen3_omni_asr/multi_en_sft.yaml). Dataset: Multi-corpus English mixture.       |

## Fine-Tuning

See the [VLM / Omni Fine-Tuning Guide](/recipes-e2e-examples/gemma-3-3n) and
[Qwen3-Omni ASR Fine-Tuning Guide](/recipes-e2e-examples/qwen3-omni-asr).

## Model Reference

### Model Architecture

| Property                  | Value                               |
| ------------------------- | ----------------------------------- |
| Task                      | Omnimodal (Text/Image/Audio/Video)  |
| Architecture              | `Qwen3OmniForConditionalGeneration` |
| Parameters                | 30B total / 3B active               |
| Hugging Face Organization | [Qwen](https://huggingface.co/Qwen) |

### Available Models

| Model                       | HF ID                                                                                         |
| --------------------------- | --------------------------------------------------------------------------------------------- |
| Qwen3-Omni 30B A3B Instruct | [`Qwen/Qwen3-Omni-30B-A3B-Instruct`](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct) |

## Related Resources

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