Zeta is a PyTorch framework designed to accelerate the development of high-performance AI models by providing modular, reusable building blocks. It targets researchers and engineers seeking to rapidly prototype, train, and optimize state-of-the-art neural networks, offering significant speedups and ease of use for complex architectures.

How It Works

Zeta emphasizes modularity and performance through a collection of optimized neural network components. It includes implementations of advanced attention mechanisms (like Sigmoid Attention and Multi-Query Attention), efficient feed-forward networks, quantization techniques (BitLinear, Niva), and fused kernels (FusedDenseGELUDense, FusedDropoutLayerNorm) to reduce overhead. The framework also provides high-level model structures like PalmE (a multimodal model) and Unet, along with utilities for hyperparameter optimization and model logging.

Quick Start & Requirements

• Install: pip3 install -U zetascale
• Prerequisites: PyTorch. GPU acceleration is recommended for performance.
• Documentation: zeta.apac.ai

Highlighted Details

• Offers implementations of SOTA components like Mamba, SwiGLU, and RelativePositionBias.
• Includes fused kernels for significant speedups in FFNs and MLPs.
• Provides a hyper_optimize utility for streamlining PyTorch optimization (compilation, quantization, mixed precision).
• Features a multimodal architecture (PalmE) combining ViT encoders with Transformer decoders.

Maintenance & Community

• Active development by Kye Gomez.
• Community engagement via Discord and Twitter.
• Clear contribution guidelines and issue tracking.
• Roadmap discussions available.

Licensing & Compatibility

• License: Apache 2.0.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The project is heavily driven by a single primary contributor, indicating a potential bus factor risk. While extensive, some components might still be in active development or lack comprehensive testing across all use cases.