zetascale 0.7.9

Transformers at zeta scales

Build High-performance, agile, and scalable AI models with modular and re-useable building blocks!

Vision

Zeta hopes to be the leading framework and library to effortlessly enable you to create the most capable and reliable foundation models out there with infinite scalability in as minmal amounts of code as possible

🤝 Schedule a 1-on-1 Session

Book a 1-on-1 Session with Kye, the Creator, to discuss any issues, provide feedback, or explore how we can improve Zeta for you.

Installation

pip install zetascale

Initiating Your Journey

Creating a model empowered with the aforementioned breakthrough research features is a breeze. Here's how to quickly materialize the renowned Flash Attention

import torch
from zeta.nn.attention import FlashAttention

q = torch.randn(2, 4, 6, 8)
k = torch.randn(2, 4, 10, 8)
v = torch.randn(2, 4, 10, 8)

attention = FlashAttention(causal=False, dropout=0.1, flash=True)
output = attention(q, k, v)

print(output.shape) 

Documentation

Click here for the documentation, it's at zeta.apac.ai

Contributing

We're dependent on you for contributions, it's only Kye maintaining this repository and it's very difficult and with that said any contribution is infinitely appreciated by not just me but by Zeta's users who dependen on this repository to build the world's best AI models. Head over to the project board to look at open features to implement or bugs to tackle!

Project Board

This weeks iteration is here