license: apache-2.0
library_name: nanovlm
tags:
- vision-language
- multimodal
- pytorch
- small-model
- efficient
- research
- VLM
model_name: nanoVLM
datasets:
- HuggingFaceM4/the_cauldron
metrics:
- accuracy
pipeline_tag: image-text-to-text
nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. The model achieves 35.3% accuracy on MMStar after training for ~6 hours on a single H100 GPU using 1.7M samples from the cauldron dataset, making it a strong baseline for low-resource VLM research.
The model is ideal for researchers and developers interested in exploring VLM training with minimal computational overhead, and serves as a perfect starting point for tinkering with multimodal architectures.
Model Architecture:
- Vision Transformer (SigLIP-B/16)
- Causal Language Model (SmolLM2)
- Modality Projection Layer
Training:
- Trained on ~1.7M samples from the
the_cauldrondataset - 6 hours on a single NVIDIA H100 GPU
- Resulting model size: 222M parameters
Evaluation:
- MMStar Accuracy: 35.3%
Usage:
Usable through the nanoVLM repository: https://github.com/huggingface/nanoVLM
For more details, see: https://github.com/huggingface/nanoVLM?tab=readme-ov-file#hub-integration
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("lusxvr/nanoVLM-222M")