From 8b8db5fed90fc575870fabbb13a651318f123412 Mon Sep 17 00:00:00 2001 From: Benoit Chevallier-Mames Date: Mon, 8 Apr 2024 19:08:28 +0200 Subject: [PATCH] reviews --- concrete-ml-inference-on-endpoints-fhe.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/concrete-ml-inference-on-endpoints-fhe.md b/concrete-ml-inference-on-endpoints-fhe.md index 345e01a17a..6fa0fb1718 100644 --- a/concrete-ml-inference-on-endpoints-fhe.md +++ b/concrete-ml-inference-on-endpoints-fhe.md @@ -17,7 +17,7 @@ More precisely, we use Hugging Face [Endpoints](https://huggingface.co/docs/infe ## Deploying a pre-compiled model -Let's start with deploying an FHE-friendly model (prepared by Zama or third parties - see "Preparing your own pre-compiled model" section below for learning how to prepare yours). +Let's start with deploying an FHE-friendly model (prepared by Zama or third parties - see "Preparing your pre-compiled model" section below for learning how to prepare yours). First, look for the model you want to deploy: We have pre-compiled a [bunch of models](https://huggingface.co/zama-fhe?#models) on Zama's HF page. Let's suppose you have chosen [concrete-ml-encrypted-decisiontree](https://huggingface.co/zama-fhe/concrete-ml-encrypted-decisiontree): As explained in the description, this pre-compiled model allows you to detect spam without looking at the message content in the clear.