Vision Language Models (VLMs)
Vision Language Models (VLMs)
Introduction
Vision Language Models (VLMs) integrate vision and language processing capabilities, enabling models to understand images and generate text descriptions, answer visual questions, and perform multimodal reasoning.
NeMo AutoModel LLM APIs can be easily extended to support VLM tasks. While most of the training setup is the same as for LLMs, some additional steps are required to prepare the data and model for VLM training.
Run VLMs with NeMo AutoModel
To run VLMs with NeMo AutoModel, use NeMo container version 26.06.00 or later. If the model you want to fine-tune requires newer source than the container provides, use a current checkout so the package and its example recipes stay on one revision:
For other installation options, see our Installation Guide.
VLMs that decode video or use Qwen / Mistral vision preprocessing also need the vlm-media extra included in the source-sync command above; it is not installed in the Docker container by default. See Media Extras.
Supported Models
NeMo AutoModel supports AutoModelForImageTextToText in the Image-Text-to-Text category.
This table combines recipe-backed checkpoints with documented model families. Dates show when the current checkpoint first appeared in a recipe, or when a documentation-only family page was added. See the combined model support log for recipe-backed checkpoints of every model type.
Fine-Tuning
All supported models can be fine-tuned using either full SFT or PEFT (LoRA) approaches. See the Gemma 3 Fine-Tuning Guide for a complete walkthrough covering dataset preparation, configuration, and multi-GPU training.
In these guides, we use the quintend/rdr-items and naver-clova-ix/cord-v2 datasets for demonstration purposes. Update the recipe YAML dataset section to use your own data. See VLM datasets and dataset overview.