> 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.

# Qwen3.5-VL

Qwen3.5-VL is Alibaba Cloud's next-generation vision language model series, including dense and MoE variants for image and multimodal understanding tasks.

|                  |                                     |
| ---------------- | ----------------------------------- |
| **Task**         | Image-Text-to-Text                  |
| **Architecture** | `Qwen3_5VLForConditionalGeneration` |
| **Parameters**   | 4B – 35B+                           |
| **HF Org**       | [Qwen](https://huggingface.co/Qwen) |

## Available Models

* **Qwen3.5-VL-4B**: 4B dense model
* **Qwen3.5-VL-9B**: 9B dense model
* **Qwen3.5-MoE**: large MoE variant (35B+)
* **Qwen3.6-27B**: 27B dense model
* **Qwen3.6-35B-A3B**: next-generation MoE variant (35B total, 3B active)

## Architectures

* `Qwen3_5VLForConditionalGeneration` — dense models
* `Qwen3_5MoeVLForConditionalGeneration` — MoE variant

## Example Recipes

| Recipe                                                                                                                                     | Dataset    | Description                     |
| ------------------------------------------------------------------------------------------------------------------------------------------ | ---------- | ------------------------------- |
| [qwen3\_5\_4b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5/qwen3_5_4b.yaml)                      | MedPix-VQA | SFT — Qwen3.5-VL 4B on MedPix   |
| [qwen3\_5\_9b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5/qwen3_5_9b.yaml)                      | MedPix-VQA | SFT — Qwen3.5-VL 9B on MedPix   |
| [qwen3\_5\_moe\_medpix.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5_moe/qwen3_5_moe_medpix.yaml) | MedPix-VQA | SFT — Qwen3.5-MoE on MedPix     |
| [qwen3\_5\_35b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5_moe/qwen3_5_35b.yaml)                | MedPix-VQA | SFT — Qwen3.5 35B on MedPix     |
| [qwen3\_6\_27b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5/qwen3_6_27b.yaml)                    | MedPix-VQA | SFT — Qwen3.6-27B on MedPix     |
| [qwen3\_6\_35b.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3_5_moe/qwen3_6_35b.yaml)                | MedPix-VQA | SFT — Qwen3.6 35B-A3B on MedPix |

## 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_5/qwen3_5_4b.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/qwen3_5/qwen3_5_4b.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](https://huggingface.co/Qwen)