> 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.

# Qwen2.5-Omni

[Qwen2.5-Omni](https://qwenlm.github.io/blog/qwen2.5-omni/) is Alibaba Cloud's omnimodal model supporting text, image, audio, and video inputs in a single unified architecture with a dense language backbone. NeMo AutoModel onboards the **Thinker** stack for audio understanding tasks such as automatic speech recognition (ASR).

|                  |                                       |
| ---------------- | ------------------------------------- |
| **Task**         | Omnimodal (Text·Image·Audio·Video)    |
| **Architecture** | `Qwen2_5OmniForConditionalGeneration` |
| **Parameters**   | 3B / 7B (dense)                       |
| **HF Org**       | [Qwen](https://huggingface.co/Qwen)   |

## Available Models

* **Qwen2.5-Omni-3B**: 3B dense backbone
* **Qwen2.5-Omni-7B**: 7B dense backbone

## Architecture

The registry wires the Qwen2.5-Omni Thinker backbone under the following architecture keys:

* `Qwen2_5OmniForConditionalGeneration`
* `Qwen2_5OmniModel`
* `Qwen2_5OmniThinkerForConditionalGeneration`

## Example HF Models

| Model           | HF ID                                                                 |
| --------------- | --------------------------------------------------------------------- |
| Qwen2.5-Omni 3B | [`Qwen/Qwen2.5-Omni-3B`](https://huggingface.co/Qwen/Qwen2.5-Omni-3B) |
| Qwen2.5-Omni 7B | [`Qwen/Qwen2.5-Omni-7B`](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) |

## Example Recipes

| Recipe                                                                                                                           | Dataset | Description                                         |
| -------------------------------------------------------------------------------------------------------------------------------- | ------- | --------------------------------------------------- |
| [ami\_sft\_3b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/audio_finetune/qwen2_5_omni_asr/ami_sft_3b.yaml) | AMI     | ASR SFT — Qwen2.5-Omni 3B on the AMI meeting corpus |
| [ami\_sft\_7b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/audio_finetune/qwen2_5_omni_asr/ami_sft_7b.yaml) | AMI     | ASR SFT — Qwen2.5-Omni 7B on the AMI meeting corpus |

## 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/audio_finetune/qwen2_5_omni_asr/ami_sft_3b.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/audio_finetune/qwen2_5_omni_asr/ami_sft_3b.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/Qwen2.5-Omni-3B](https://huggingface.co/Qwen/Qwen2.5-Omni-3B)
* [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B)