> 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-VL

[Qwen2.5-VL](https://qwenlm.github.io/blog/qwen2.5-vl/) is Alibaba Cloud's vision language model series supporting image and video understanding. It features dynamic resolution processing and integrates with the Qwen2.5 language backbone.

|                  |                                     |
| ---------------- | ----------------------------------- |
| **Task**         | Image-Text-to-Text                  |
| **Architecture** | `Qwen2_5VLForConditionalGeneration` |
| **Parameters**   | 2B – 72B                            |
| **HF Org**       | [Qwen](https://huggingface.co/Qwen) |

## Available Models

* **Qwen2.5-VL-72B-Instruct**
* **Qwen2.5-VL-32B-Instruct**
* **Qwen2.5-VL-7B-Instruct**
* **Qwen2.5-VL-3B-Instruct**
* **Qwen2-VL-7B-Instruct**, **Qwen2-VL-2B-Instruct** (Qwen2 VL)

## Architectures

* `Qwen2_5VLForConditionalGeneration` — Qwen2.5-VL
* `Qwen2VLForConditionalGeneration` — Qwen2-VL

## Example HF Models

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

## Example Recipes

| Recipe                                                                                                                                | Dataset   | Description                      |
| ------------------------------------------------------------------------------------------------------------------------------------- | --------- | -------------------------------- |
| [qwen2\_5\_vl\_3b\_rdr.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen2_5/qwen2_5_vl_3b_rdr.yaml) | rdr-items | SFT — Qwen2.5-VL 3B on RDR Items |

## 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/qwen2_5/qwen2_5_vl_3b_rdr.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:26.04.00
```

**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/qwen2_5/qwen2_5_vl_3b_rdr.yaml
```

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

## Fine-Tuning

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

## Hugging Face Model Cards

* [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
* [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)