Qwen2.5-Omni#

Qwen2.5-Omni is Alibaba Cloud’s omnimodal model supporting text, image, audio, and video inputs in a single unified architecture with a dense language backbone. NeMo AutoModel onboards the Thinker stack for audio understanding tasks such as automatic speech recognition (ASR).

Task

Omnimodal (Text·Image·Audio·Video)

Architecture

Qwen2_5OmniForConditionalGeneration

Parameters

3B / 7B (dense)

HF Org

Qwen

Available Models#

  • Qwen2.5-Omni-3B: 3B dense backbone

  • Qwen2.5-Omni-7B: 7B dense backbone

Architecture#

The registry wires the Qwen2.5-Omni Thinker backbone under the following architecture keys:

  • Qwen2_5OmniForConditionalGeneration

  • Qwen2_5OmniModel

  • Qwen2_5OmniThinkerForConditionalGeneration

Example HF Models#

Model

HF ID

Qwen2.5-Omni 3B

Qwen/Qwen2.5-Omni-3B

Qwen2.5-Omni 7B

Qwen/Qwen2.5-Omni-7B

Example Recipes#

Recipe

Dataset

Description

ami_sft_3b.yaml

AMI

ASR SFT — Qwen2.5-Omni 3B on the AMI meeting corpus

ami_sft_7b.yaml

AMI

ASR SFT — Qwen2.5-Omni 7B on the AMI meeting corpus

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/audio_finetune/qwen2_5_omni_asr/ami_sft_3b.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/audio_finetune/qwen2_5_omni_asr/ami_sft_3b.yaml

See the Installation Guide and Omni Fine-Tuning Guide.

Fine-Tuning#

See the VLM / Omni Fine-Tuning Guide.

Hugging Face Model Cards#