Gemma 3 VL / Gemma 3n#

Gemma 3 VL is Google’s multimodal extension of Gemma 3, supporting image-text inputs for tasks like image captioning and visual question answering. Gemma 3n is a next-generation efficiency-focused variant.

Task

Image-Text-to-Text

Architecture

Gemma3ForConditionalGeneration

Parameters

4B – 27B

HF Org

google

Available Models#

  • Gemma 3 27B IT (VL)

  • Gemma 3 4B IT (VL)

  • Gemma 3n 4B (VL)

Architecture#

  • Gemma3ForConditionalGeneration

Example HF Models#

Model

HF ID

Gemma 3 4B IT

google/gemma-3-4b-it

Gemma 3 27B IT

google/gemma-3-27b-it

Example Recipes#

Recipe

Dataset

Description

gemma3_vl_4b_cord_v2.yaml

cord-v2

SFT — Gemma 3 4B VL on CORD-v2

gemma3_vl_4b_cord_v2_peft.yaml

cord-v2

LoRA — Gemma 3 4B VL on CORD-v2

gemma3_vl_4b_cord_v2_megatron_fsdp.yaml

cord-v2

SFT — Gemma 3 4B VL with MegatronFSDP

gemma3_vl_4b_medpix.yaml

MedPix-VQA

SFT — Gemma 3 4B VL on MedPix

gemma3n_vl_4b_medpix.yaml

MedPix-VQA

SFT — Gemma 3n 4B VL on MedPix

gemma3n_vl_4b_medpix_peft.yaml

MedPix-VQA

LoRA — Gemma 3n 4B VL 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/gemma3/gemma3_vl_4b_cord_v2.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/gemma3/gemma3_vl_4b_cord_v2.yaml

See the Installation Guide and VLM Fine-Tuning Guide.

Fine-Tuning#

See the Gemma 3 & Gemma 3n Fine-Tuning Guide for detailed instructions on dataset preparation, configuration, and multi-GPU training.

Hugging Face Model Cards#