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# RuDOLPH-350M (Medium) |
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RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP |
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<img src="https://raw.githubusercontent.com/shonenkov/ru-dolph/master/pics/rudolph-generated.png?token=AHV2MCORXON3DROFL7FBBQ3B4CW4S" height="60" border="2"/> |
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Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams. |
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* Task: `text2image generation`; `self reranking`; `text ranking`; `image ranking`; `image2text generation`; `zero-shot image classification`, `text2text generation`; |
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* Language: `Russian` |
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* Type: `encoder-decoder` |
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* Num Parameters: `350M` |
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* Training Data Volume: `35 million text-image pairs` |
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# Model Description |
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**Ru**ssian **D**iffusion **O**n **L**anguage **P**icture **H**yper-modality (RuDOLPH) Transformer 350M is a fast and light text-image-text transformer (350M GPT-3) 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. |
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*(!!!) 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* |
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# Sparse Attention Mask |
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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 image condition without attention to left text. |
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<img src="https://raw.githubusercontent.com/shonenkov/ru-dolph/master/pics/attention_masks.png?token=AHV2MCP7BH3CQBAK74UVA7TB4CXQE" height="40" border="2"/> |
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# Authors |
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+ Alex Shonenkov: [Github](https://github.com/shonenkov), [Kaggle GM](https://www.kaggle.com/shonenkov) |
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+ Michael Konstantinov: [Mishin Learning](https://t.me/mishin_learning), [Transformer Community](https://transformer.community/) |