Concrete ML v1.2.0: Hybrid Deployment and Inference Speed Improvements

Concrete ML version 1.2 introduces several advancements, including hybrid deployment with Fully Homomorphic Encryption (FHE) and support for K-nearest neighbor (KNN) classification, aimed at enhancing model security and deployment flexibility. The hybrid deployment allows models, including Large Language Models (LLMs) and Convolutional Neural Networks (CNNs), to be partially deployed on-premise and in the cloud, protecting both confidential data and model intellectual property. This version also incorporates optimizations in neural networks, such as right bit-shift and quantization-aware training, significantly improving inference speed while maintaining accuracy. The KNN classification is performed on encrypted data, ensuring data privacy through encrypted distance calculations. Additionally, the development pipeline has transitioned to a public repository to expedite bug fixes, enabling developers to access the latest code improvements.