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

Available Models

• Qwen3.5-VL-4B: 4B dense model

• Qwen3.5-VL-9B: 9B dense model

• Qwen3.5-MoE: large MoE variant (35B+)

Architectures

• Qwen3_5VLForConditionalGeneration — dense models

• Qwen3_5MoeVLForConditionalGeneration — MoE variant

Example Recipes

Recipe	Dataset	Description
qwen3_5_4b.yaml	MedPix-VQA	SFT — Qwen3.5-VL 4B on MedPix
qwen3_5_9b.yaml	MedPix-VQA	SFT — Qwen3.5-VL 9B on MedPix
qwen3_5_moe_medpix.yaml	MedPix-VQA	SFT — Qwen3.5-MoE on MedPix
qwen3_5_35b.yaml	MedPix-VQA	SFT — Qwen3.5 35B on MedPix

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_5/qwen3_5_4b.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_5/qwen3_5_4b.yaml

See the Installation Guide and VLM Fine-Tuning Guide.

Fine-Tuning

See the VLM Fine-Tuning Guide.

Hugging Face Model Cards

• Qwen