[Colab]

One Hyper-Tasking Transformer can be creative as DALL-E and GPT-3 and smart as CLIP

RUssian Decoder On Language Picture Hyper-tasking (RUDOLPH) is a 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 model is a generalized multi-tasking model, i.e., the model that can solve almost all tasks within supported modalities, mandatory including mutual pairwise translations between modalities (two modalities in case of RUDOLPH: images and Russian texts).

• 350M (RUDOLPH)
• 1.3B (RUDOLPH)
• 2.7B (RUDOLPH)
• 2.7B_FBC2 (RUDOLPH-fine-tuned)

The following table shows the values of the parameters corresponding to different RUDOLPH versions.

row - col - row - [last] conv

row - col - row - [last] conv

row - col - row - [last] conv

pip install rudolph==0.0.1rc10

Usage and fine-tuning examples for different versions of RUDOLPH can be found in jupyters folder.

@misc{github2022ruDolph,
  title         = {RUDOLPH: One Hyper-Tasking Transformer can be creative as DALL-E and GPT-3 and smart as CLIP},
  author        = {AIRI},
  year          = {2022},
  howpublished  = {\url{https://github.com/ai-forever/ru-dolph}},
}