From 5bb0c1167fc6d84d151fa97c40957005cb1c7ce9 Mon Sep 17 00:00:00 2001
From: PENG Bo <33809201+BlinkDL@users.noreply.github.com>
Date: Sat, 18 Jun 2022 08:49:48 +0800
Subject: [PATCH] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 87530e4..3a25bda 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ kv / k is the memory mechanism. The token with high k can be remembered for a lo
 
 RWKV v2 is parallelizable because the time-decay of each channel is data-independent (and trainable). For example, in usual RNN you can adjust the time-decay of a channel from say 0.8 to 0.5 (these are called "gates"), while in RWKV v2 you simply move the information from a W-0.8-channel to a W-0.5-channel to achieve the same effect.
 
-## RWKV v2.x improvements (not yet uploaded to github. used in the latest 1.5B run)
+### RWKV v2.x improvements (not yet uploaded to github. used in the latest 1.5B run)
 
 * Use different trainable TimeMix factors for R / K / V.
 * Use preLN instead of postLN.