Qwen3-Omni#

Qwen3-Omni is Alibaba Cloud’s omnimodal model supporting text, image, audio, and video inputs in a single unified architecture with a MoE language backbone.

Task

Omnimodal (Text·Image·Audio·Video)

Architecture

Qwen3OmniForConditionalGeneration

Parameters

30B total / 3B active

HF Org

Qwen

Available Models#

  • Qwen3-Omni-30B-A3B: 30B total, 3B activated (MoE)

Architecture#

  • Qwen3OmniForConditionalGeneration

Example HF Models#

Model

HF ID

Qwen3-Omni 30B A3B

Qwen/Qwen3-Omni-30B-A3B

Example Recipes#

Recipe

Dataset

Description

qwen3_omni_moe_30b_te_deepep.yaml

MedPix-VQA

SFT — Qwen3-Omni 30B with TE + DeepEP

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_omni_moe_30b_te_deepep.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_omni_moe_30b_te_deepep.yaml

See the Installation Guide and Omni Fine-Tuning Guide.

Fine-Tuning#

See the VLM / Omni Fine-Tuning Guide.

Hugging Face Model Cards#