Update README.md

README.md

@@ -1,28 +1,54 @@
-# RuDOLPH-1.3B (Large)
+---
+tags:
+- RUDOLPH
+- text-image
+- image-text
+- decoder
+---
+# RUDOLPH-1.3B (Large)
 
-RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP
+RUDOLPH: One Hyper-Tasking Transformer Can be Creative as DALL-E and GPT-3 and Smart as CLIP
 
-<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/rudolph-generated.png" height="60" border="2"/>
+<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/RUDOLPH.png" height="60" border="2"/>
 
-Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.  
+Model was trained by [Sber AI](https://github.com/sberbank-ai) team.  
 * 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.
+**RU**ssian **D**ecoder **O**n **L**anguage **P**icture **H**yper-tasking (**RUDOLPH**) **1.3B** is a large text-image-text transformer designed for an easy fine-tuning for a range of tasks: from generating images by text description and image classification to visual question answering and more. This model demonstrates the power of Hyper-tasking Transformers.
+
+*Hyper-tasking means generalized multi-tasking, e.g., the model that can solve almost all tasks within supported modalities (two modalities in case of RUDOLPH: images and Russian texts).*
+
+* Tasks: ` text2image generation, self reranking, text ranking, image ranking, image2text generation, zero-shot image classification, text2text generation, text-qa, math-qa, image captioning, image generation, text-in-the-wild, vqa, and so on`
+* Language: ` Russian`
+* Type: ` decoder`
+* Num Parameters: ` 1.3B`
+* Training Data Volume: ` 119 million text-image pairs, 60 million text paragraphs`
+
+# Details of architecture
+
+### Parameters
+
+<img src=https://raw.githubusercontent.com/ai-forever/ru-dolph/master/pics/scheme-rudolph_13b.jpg height="20" border="2"/>
+
+The maximum sequence length that this model may be used with depends on the modality and stands for 128 - 1024 - 128 for the left text tokens, image tokens, and right text tokens, respectively.
+
+RUDOLPH 1.3B is a Transformer-based decoder model with the following parameters:
 
-*(!!!) 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*
+* num\_layers (24) — Number of hidden layers in the Transformer decoder.
+* hidden\_size (2048) — Dimensionality of the hidden layers.
+* num\_attention\_heads (16) — Number of attention heads for each attention layer.
 
 # 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)
+<img src="https://raw.githubusercontent.com/ai-forever/ru-dolph/master/pics/attention_mask_13b.png" height="20" border="2"/>
 
 # Authors