text-to-text-transfer-transformer VS dalle-mini

Google released the T5 paper about 5 years ago:

https://arxiv.org/abs/1910.10683

This included full model weights along with a detailed description of the dataset, training process, and ablations that led them to that architecture. T5 was state-of-the-art on many benchmarks when it was released, but it was of course quickly eclipsed by GPT-3.

Following GPT-3, it became much more common for labs to not release full details or model weights. Prior to that, it was common practice from Google (BERT, T5), Meta (BART), OpenAI (GPT1, GPT2) and others to release full training details and model weights.

[2] - https://arxiv.org/abs/1910.10683

T5-style relative encodings https://arxiv.org/abs/1910.10683

11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research)

You can find a link to the paper here: https://arxiv.org/abs/1910.10683

The Text-to-Text Transformer is a type of neural network architecture that is particularly well-suited for natural language processing tasks involving the generation of text. It was introduced in the paper "Attention is All You Need" by Vaswani et al. and has since become a popular choice for many NLP tasks, including language translation, summarization, and text generation

Found relevant code at https://github.com/google-research/text-to-text-transfer-transformer + all code implementations here

Mini-Gemini is a bit of a confusing name.

Reminds me of how DALL·E Mini came out three years ago and eventually had to rename itself to Craiyon https://github.com/borisdayma/dalle-mini

I wouldnt consider Craiyon to be high tier equipment

Made with : https://www.craiyon.com/

AI Site

https://www.craiyon.com/ try it, and it's not even that good a text to image generator.

For example used just your prompt on Craiyon , guess you can try on replicate the free sd demos for example.