# RuDOLPH-1.3B (Large) RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams. * Task: `text2image generation`; `self reranking`; `text ranking`; `image ranking`; `image2text generation`; `zero-shot image classification`, `text2text generation`; * Language: `Russian` * Type: `decoder` * Num Parameters: `2.7B` * Training Data Volume: `119 million text-image pairs; 60 million text paragraphs` # Model Description **Ru**ssian **D**iffusion **O**n **L**anguage **P**icture **H**yper-modality (RuDOLPH) 1.3B is a large version of fast and light text-image-text transformer designed for a quick and easy fine-tuning setup for the solution of various tasks: from generating images by text description and image classification to visual question answering and more. This model demonstrates the power of Hyper-modality Transformers. *(!!!) Hyper-modality means generalized multi-modal, e.g., model that consists of two multi-modal parts: text-2-image and image-2-text becomes text and image hyper-modality model* # Sparse Attention Mask The primary proposed method is to modify the sparse transformer's attention mask to better control multi-modalities and up to the next level with "hyper-modality". It allows us to calculate the transitions of modalities in both directions, unlike another similar work DALL-E Transformer, which used only one direction, "text to image". The proposed "image to right text" direction is achieved by extension sparse attention mask to the right for auto-repressively text generation with both image and left text condition. ![rudolph_masks_13b.png](https://s3.amazonaws.com/moonup/production/uploads/1663698965167-5f91b1208a61a359f44e1851.png) # Authors + Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov)