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fhe-endpoints.md

@@ -19,7 +19,7 @@ More precisely, we use Hugging Face [Endpoints](https://huggingface.co/docs/infe
 
 Let's start with deploying an FHE-friendly model (prepared by Zama or third parties - see [Preparing your pre-compiled model](#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.
+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 (or you can [find them](https://huggingface.co/models?other=concrete-ml) with tags). 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.
 
 Like any other model available on the Hugging Face platform, select _Deploy_ and then _Inference Endpoint (dedicated)_: