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:26.02.00

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.

Hugging Face Model Cards#