Model CoverageVision Language Models (VLMs)

Qwen3.5-VL

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

TaskImage-Text-to-Text
ArchitectureQwen3_5VLForConditionalGeneration
Parameters4B – 35B+
HF OrgQwen

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

RecipeDatasetDescription
qwen3_5_4b.yamlMedPix-VQASFT — Qwen3.5-VL 4B on MedPix
qwen3_5_9b.yamlMedPix-VQASFT — Qwen3.5-VL 9B on MedPix
qwen3_5_moe_medpix.yamlMedPix-VQASFT — Qwen3.5-MoE on MedPix
qwen3_5_35b.yamlMedPix-VQASFT — Qwen3.5 35B on MedPix
qwen3_6_27b.yamlMedPix-VQASFT — Qwen3.6-27B on MedPix
qwen3_6_35b.yamlMedPix-VQASFT — Qwen3.6 35B-A3B 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

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:26.04.00

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.

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