> 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

[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.

|                  |                                     |
| ---------------- | ----------------------------------- |
| **Task**         | Omnimodal (Text·Image·Audio·Video)  |
| **Architecture** | `Qwen3OmniForConditionalGeneration` |
| **Parameters**   | 30B total / 3B active               |
| **HF Org**       | [Qwen](https://huggingface.co/Qwen) |

## Available Models

* **Qwen3-Omni-30B-A3B-Instruct**: 30B total, 3B activated (MoE)

## Architecture

* `Qwen3OmniForConditionalGeneration`

## Example HF Models

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

## Example Recipes

| Recipe                                                                                                                                                     | Dataset    | Description                           |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | ------------------------------------- |
| [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) | MedPix-VQA | SFT — Qwen3-Omni 30B with TE + DeepEP |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

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

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

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

See the [Installation Guide](/get-started/installation) and [Omni Fine-Tuning Guide](/recipes-e2e-examples/gemma-3-3n).

## Fine-Tuning

See the [VLM / Omni Fine-Tuning Guide](/recipes-e2e-examples/gemma-3-3n).

## Hugging Face Model Cards

* [Qwen/Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct)