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:26.06.00

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.