Zeta: Assemble PyTorch models from reusable components

Understand the project

Practical overview

Use Zeta during model development when you need modular PyTorch components or want to assemble an architecture from existing building blocks.

PyTorch developers assembling custom transformer or other neural-network architectures.
Model developers who want reusable components such as attention, feed-forward layers, vision embeddings or quantization modules.

Compose custom models from reusable architecture components.
Prototype model designs by connecting attention, feed-forward and other neural-network modules.
Explore documented vision, transformer and quantization building blocks in a PyTorch project.

Before you choose

The public GitHub repository specifies the Apache-2.0 license. Its README documents installation through pip with the package name `zetascale`.

Zeta is a developer framework that supplies components; the documented examples require writing Python code and working with PyTorch tensors.
The component range includes individual layers as well as examples of complete architectures, so it can serve both targeted component use and broader model composition.
The repository metadata specifies Apache-2.0; the README’s displayed license badge says MIT, so confirm the applicable license in the repository before relying on licensing terms.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the documented package and instantiate a Zeta attention layer for a tensor input.

Install the package with the command below.
Documented command
```
pip3 install -U zetascale
```
Import torch and MultiQueryAttention from zeta.
Initialize MultiQueryAttention with dim=512 and heads=8.
Create a tensor with torch.randn(2, 4, 512) and pass it to the model.

The README example receives an output tensor from the multi-query attention module and prints its shape.

This example demonstrates a component forward pass, not model training or deployment.
The README does not establish performance or compatibility across environments.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 13 October 2026
Original website title: kyegomez/zeta · Official homepage title · retained in its source language