From 6ae08c0312e2e00cc242ff4f737f14557642ae9f Mon Sep 17 00:00:00 2001
From: scap3yvt <149599669+scap3yvt@users.noreply.github.com>
Date: Mon, 18 Mar 2024 16:05:52 -0400
Subject: [PATCH] fix html rendering

---
 docs/usage.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/usage.md b/docs/usage.md
index cc0c3e52d..874dd007e 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -405,10 +405,10 @@ Once you have a model definition, it is easy to perform federated learning using
 
 Once you have a trained model, you can perform [federated evaluation](https://flower.dev/docs/tutorial/Flower-0-What-is-FL.html#Federated-evaluation) using [MedPerf](https://medperf.org/). Follow the tutorial in [this page](https://docs.medperf.org/mlcubes/gandlf_mlcube/) to get started.
 
-!!! note
-  Please note that in order to create a GaNDLF MLCube, for technical reasons, you need write access to the GaNDLF package. With a virtual environment this should be automatic. See the [installation instructions](./setup.md#installation).
+**Notes**:
+-  To create a GaNDLF MLCube, for technical reasons, you need write access to the GaNDLF package. This should be automatic while using a virtual environment that you have set up. See the [installation instructions](./setup.md#installation) for details.
+-  This needs GaNDLF to be initialized as an MLCube. See [the mlcube instructions](https://docs.medperf.org/mlcubes/gandlf_mlcube/) for details.
 
-https://docs.medperf.org/mlcubes/gandlf_mlcube/
 ## Running with Docker
 
 The usage of GaNDLF remains generally the same even from Docker, but there are a few extra considerations.