| # RuDOLPH-1.3B (Large) | |
| RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP | |
| <img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/rudolph-generated.png" height="60" border="2"/> | |
| 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: `1.3B` | |
| * 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. | |
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| # Authors | |
| + Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov) |