Model Detail
zeta-2
—zeta-2 is a code generation model with 8B parameters released by zed-industries. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
zeta-2 ships with 8B parameters. Total weight footprint is approximately 8.3 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
zeta-2 is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Zeta-Lite: A Concurrent, Branchable In-Browser SQL Database for Agentic Memory
arXiv:2609.01818v1 Announce Type: cross Abstract: The browser has become a first-class database host: applications increasingly want to store, query, and reason over structured data entirely on the client - for privacy, offline operation, local-first collaboration, and, most recently, as durable mem
Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning
arXiv:2606.14187v2 Announce Type: replace Abstract: Large-scale neural network training increasingly relies on matrix-aware optimizers that exploit the structure of weight parameters beyond element-wise adaptation. However, existing matrix-aware methods such as Muon have an underappreciated vulnerab
Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability
arXiv:2605.30482v1 Announce Type: new Abstract: Machine learning is increasingly used in mathematical discovery, but in mathematics the desired output is often not a prediction itself, but an explicit construction that can be checked independently. We study this setting through the zeta map on Dyck
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
arXiv:2604.17581v1 Announce Type: new Abstract: How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress increasingly depends on predicting when additional data will substantially improve performance. In prac
