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# Qwen3-VL-235B-A22B-Instruct

> Use NeMo AutoModel to fine-tune Qwen3-VL-235B-A22B-Instruct for VLM workflows with checked-in image-text-to-text recipes and documented distributed settings.

[Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) has a checked-in NeMo AutoModel recipe for image-text-to-text.
The Hugging Face configuration declares the `Qwen3VLMoeForConditionalGeneration` architecture.

## Fine-Tune Qwen3-VL-235B-A22B-Instruct

Follow the [installation instructions](/get-started/installation), then run the recipe from the repository root:

```bash
automodel examples/vlm_finetune/qwen3/qwen3_vl_moe_235b.yaml --nproc-per-node 8
```

Use the [Slurm launcher guide](/job-launchers/slurm-cluster) for the multi-node run.

## Choose a Workflow

| Goal                                         | Start Here                                                                                                                               |
| -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| Run the primary recipe for this model        | Use the [recipe configuration](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_finetune/qwen3/qwen3_vl_moe_235b.yaml).   |
| Try another checked-in recipe for this model | Use the [alternate recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_benchmark/qwen/qwen3_vl_235b_te_deepep.yaml). |

### Configuration

| Setting  | Configuration                                               |
| -------- | ----------------------------------------------------------- |
| Hardware | -                                                           |
| Strategy | FSDP2; tp\_size=1, pp\_size=4, cp\_size=1, ep\_size=32      |
| Nodes    | 32                                                          |
| Features | Transformer Engine, HybridEP (dispatcher=hybridep)          |
| Advanced | attention=sdpa, linear=te, experts=gmm, dispatcher=hybridep |

### More Recipes for This Model

* [`qwen3_vl_235b_te_deepep.yaml`](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_benchmark/qwen/qwen3_vl_235b_te_deepep.yaml)
* [`qwen3vl235b_lora.yaml`](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/vlm_benchmark/qwen/qwen3vl235b_lora.yaml)

## Model Reference

### Model Architecture

| Property     | Value                                |
| ------------ | ------------------------------------ |
| Task         | Image-text-to-text                   |
| Architecture | `Qwen3VLMoeForConditionalGeneration` |

### Available Models

* [`Qwen/Qwen3-VL-235B-A22B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct)

## Related Resources

* [Vision-language model fine-tuning guide](/recipes-e2e-examples/gemma-3-3n)