LLaVA

LLaVA (Large Language and Vision Assistant) is a pioneering open-source multimodal model connecting a vision encoder to a language model via a projection layer. Multiple versions and variants are supported via the llava-hf organization on Hugging Face.


Task	Image-Text-to-Text
Architecture	`LlavaForConditionalGeneration` / `LlavaNextForConditionalGeneration`
Parameters	7B – 34B
HF Org	llava-hf

Available Models

LLaVA-1.5 (LlavaForConditionalGeneration): 7B, 13B
LLaVA-1.6 / LLaVA-NeXT (LlavaNextForConditionalGeneration): 7B, 34B
LLaVA-NeXT-Video (LlavaNextVideoForConditionalGeneration): 7B
LLaVA-OneVision (LlavaOnevisionForConditionalGeneration): 7B

Architectures

LlavaForConditionalGeneration — LLaVA 1.5
LlavaNextForConditionalGeneration — LLaVA-NeXT / 1.6
LlavaNextVideoForConditionalGeneration — LLaVA-NeXT-Video
LlavaOnevisionForConditionalGeneration — LLaVA-OneVision

Example HF Models

Model	HF ID
LLaVA 1.5 7B	`llava-hf/llava-1.5-7b-hf`
LLaVA 1.5 13B	`llava-hf/llava-1.5-13b-hf`
LLaVA-NeXT Mistral 7B	`llava-hf/llava-v1.6-mistral-7b-hf`
LLaVA-NeXT 34B	`llava-hf/llava-v1.6-34b-hf`
LLaVA-NeXT-Video 7B	`llava-hf/LLaVA-NeXT-Video-7B-hf`
LLaVA-OneVision 7B	`llava-hf/llava-onevision-qwen2-7b-ov-hf`

Try with NeMo AutoModel

Install NeMo AutoModel and follow the fine-tuning guide to configure a recipe for this model.

1. Install (full instructions):

$ pip install nemo-automodel

2. Clone the repo to get example recipes you can adapt:

$ git clone https://github.com/NVIDIA-NeMo/Automodel.git
$ cd Automodel

3. Fine-tune by adapting a base VLM recipe — override the model ID on the CLI:

$ automodel --nproc-per-node=8 examples/vlm_finetune/gemma3/gemma3_vl_4b_cord_v2.yaml \
>   --model.pretrained_model_name_or_path <MODEL_HF_ID>

Replace <MODEL_HF_ID> with the model ID from Example HF Models above.

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. The recipes are at /opt/Automodel/examples/ — navigate there:

$ cd /opt/Automodel

3. Fine-tune:

$ automodel --nproc-per-node=8 examples/vlm_finetune/gemma3/gemma3_vl_4b_cord_v2.yaml \
>   --model.pretrained_model_name_or_path <MODEL_HF_ID>

See the Installation Guide and VLM Fine-Tuning Guide.

Fine-Tuning

See the VLM Fine-Tuning Guide.