Qwen2.5-VL#

Qwen2.5-VL is Alibaba Cloud’s vision language model series supporting image and video understanding. It features dynamic resolution processing and integrates with the Qwen2.5 language backbone.

Task

Image-Text-to-Text

Architecture

Qwen2_5VLForConditionalGeneration

Parameters

2B – 72B

HF Org

Qwen

Available Models#

  • Qwen2.5-VL-72B-Instruct

  • Qwen2.5-VL-32B-Instruct

  • Qwen2.5-VL-7B-Instruct

  • Qwen2.5-VL-3B-Instruct

  • Qwen2-VL-7B-Instruct, Qwen2-VL-2B-Instruct (Qwen2 VL)

Architectures#

  • Qwen2_5VLForConditionalGeneration — Qwen2.5-VL

  • Qwen2VLForConditionalGeneration — Qwen2-VL

Example HF Models#

Model

HF ID

Qwen2.5-VL 3B Instruct

Qwen/Qwen2.5-VL-3B-Instruct

Qwen2.5-VL 7B Instruct

Qwen/Qwen2.5-VL-7B-Instruct

Qwen2-VL 7B Instruct

Qwen/Qwen2-VL-7B-Instruct

Example Recipes#

Recipe

Dataset

Description

qwen2_5_vl_3b_rdr.yaml

rdr-items

SFT — Qwen2.5-VL 3B on RDR Items

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/qwen2_5/qwen2_5_vl_3b_rdr.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/qwen2_5/qwen2_5_vl_3b_rdr.yaml

See the Installation Guide and VLM Fine-Tuning Guide.

Fine-Tuning#

See the VLM Fine-Tuning Guide.

Hugging Face Model Cards#