Towards Encrypted Large Language Models with FHE
This Hugging Face blog post explores applying Fully Homomorphic Encryption (FHE) to Large Language Models, enabling inference on encrypted data without exposing plaintext inputs to the server. The approach aims to address privacy concerns in cloud-based LLM deployments by allowing computations to occur directly on ciphertext. The post likely covers the technical challenges of adapting transformer architectures to FHE constraints and presents early feasibility results.
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Related events (8)
Running Privacy-Preserving Inferences on Hugging Face Endpoints
Hugging Face has published a blog post describing the integration of Fully Homomorphic Encryption (FHE) with its Inference Endpoints service, enabling privacy-preserving ML inference where data remains encrypted throughout computation. The approach allows clients to send encrypted inputs to a hosted model without the server ever seeing plaintext data. This represents a practical deployment of FHE-based ML, a technique that has historically been too slow for production use but is gaining traction with recent optimizations.
Sentiment Analysis on Encrypted Data with Homomorphic Encryption
This Hugging Face blog post demonstrates running sentiment analysis on fully homomorphic encrypted (FHE) data, enabling inference without the server ever seeing plaintext inputs. The approach combines a fine-tuned NLP model with Concrete-ML, a library that compiles ML models to FHE circuits. This represents a practical demonstration of privacy-preserving ML inference at the application layer.
Deploy LLMs with Hugging Face Inference Endpoints
Hugging Face published a guide on deploying large language models using their Inference Endpoints service. The post covers how to set up scalable, production-ready LLM deployments with minimal infrastructure overhead. It targets developers looking to move from experimentation to hosted inference without managing raw compute.
Optimizing your LLM in production
A Hugging Face blog post covering practical techniques for optimizing large language models in production environments. The post likely addresses inference efficiency methods such as quantization, batching, caching, and hardware utilization strategies. It serves as a practitioner-oriented guide for deploying LLMs at scale.
Very Large Language Models and How to Evaluate Them
This Hugging Face blog post from October 2022 discusses approaches to zero-shot evaluation of large language models hosted on the Hub. It covers methodologies for benchmarking LLMs without task-specific fine-tuning, addressing the practical challenges of evaluating very large models at scale. The post situates evaluation tooling within the broader ecosystem of open model hosting and assessment.
Federated Learning using Hugging Face and Flower
This Hugging Face blog post describes how to combine the Hugging Face ecosystem with the Flower federated learning framework to train models across distributed, privacy-preserving data silos. It provides a practical walkthrough of integrating Transformers and Datasets libraries with Flower's federated training loop. The post targets practitioners looking to apply federated learning to NLP and other ML tasks without centralizing sensitive data.
Deep Learning over the Internet: Training Language Models Collaboratively
This Hugging Face blog post describes a framework for training large language models collaboratively across volunteer compute contributed over the internet. The approach addresses the challenge of enabling distributed participants with heterogeneous hardware to jointly train models without centralized infrastructure. It represents an early exploration of decentralized training as an alternative to large-scale private compute clusters.
Investing in Performance: Fine-tune small models with LLM insights — a CFM case study
This Hugging Face blog post presents a case study from CFM (Capital Fund Management) on using large language model outputs to guide fine-tuning of smaller, more efficient models for financial applications. The approach leverages LLM-generated signals or labels to train compact models that can be deployed at lower cost and latency. The case study illustrates an enterprise pattern of distilling LLM capabilities into task-specific smaller models for production use.