Qwen3-VL / Qwen3-VL-MoE

Qwen3-VL is Alibaba Cloud’s third-generation vision language model series. The MoE variant activates a fraction of parameters per token for efficient large-scale inference.


Task	Image-Text-to-Text
Architecture	`Qwen3VLForConditionalGeneration`
Parameters	4B – 235B
HF Org	Qwen

Available Models

Qwen3-VL-8B-Instruct: 8B
Qwen3-VL-4B-Instruct: 4B
Qwen3-VL-MoE-30B: 30B total (MoE)
Qwen3-VL-MoE-235B: 235B total (MoE)

Architecture

Qwen3VLForConditionalGeneration

Example HF Models

Model	HF ID
Qwen3-VL 4B Instruct	`Qwen/Qwen3-VL-4B-Instruct`
Qwen3-VL 8B Instruct	`Qwen/Qwen3-VL-8B-Instruct`

Example Recipes

Recipe	Dataset	Description
qwen3_vl_4b_instruct_rdr.yaml	rdr-items	SFT — Qwen3-VL 4B on RDR Items
qwen3_vl_8b_instruct_rdr.yaml	rdr-items	SFT — Qwen3-VL 8B on RDR Items
qwen3_vl_moe_30b_te_deepep.yaml	MedPix-VQA	SFT — Qwen3-VL-MoE 30B with TE + DeepEP
qwen3_vl_moe_235b.yaml	MedPix-VQA	SFT — Qwen3-VL-MoE 235B

Try with NeMo AutoModel

1. Install (full instructions):

$ pip install nemo-automodel

2. Clone the repo to get the example recipes:

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

3. Run the recipe from inside the repo:

$ automodel --nproc-per-node=8 examples/vlm_finetune/qwen3/qwen3_vl_4b_instruct_rdr.yaml

Run with Docker

1. Pull the container and mount a checkpoint directory:

$ 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):

$ cd /opt/Automodel

3. Run the recipe:

$ automodel --nproc-per-node=8 examples/vlm_finetune/qwen3/qwen3_vl_4b_instruct_rdr.yaml

See the Installation Guide and VLM Fine-Tuning Guide.

Fine-Tuning

See the VLM Fine-Tuning Guide.