NanoVLM SmolLM2 135M Siglip Base Patch16 224

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- **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. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. **Usage:** Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("cultural-arts/nanoVLM-SmolLM2-135M-siglip-base-patch16-224") ```

Source context: 8 downloads · 0 likes · Pipeline image-text-to-text · Library nanovlm · Repo cultural-arts/nanoVLM-SmolLM2-135M-siglip-base-patch16-224

Source context: 104 downloads · 0 likes · Pipeline image-text-to-text · Library nanovlm · Repo cultural-arts/nanoVLM-SmolLM2-135M-siglip-base-patch16-224