After five years of work, the team is happy to announce the Zama Confidential Blockchain Protocol and release of our Testnet.
Today, we're taking a decisive step toward the future of confidential blockchain, and it involves our new partners at OpenZeppelin
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In this tutorial, Zama team member Andrei Stoian, shows you how to fine-tune LLM models on encrypted data using Concrete ML.
fhEVM v0.6 introduces expanded type support, a more robust input mechanism, and enhanced configurability for fhEVM deployment.
Concrete ML v1.8 improves the speed and usability of LLM hybrid fine-tuning with an optimized FHE backend and a new API.
Concrete v2.9 enhances the interoperability between TFHE-rs and Concrete, and adds support for Python 3.12
TFHE-rs v0.11 brings several major improvements including FHE strings, faster Zero Knowledge Proof, and encrypted arrays on GPU.
With these releases, Zama continues to build its suite of products to make homomorphic encryption accessible, easy, and fast.