diff --git a/docs/Project.toml b/docs/Project.toml
index aa29942f9..f792ff713 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -4,6 +4,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterVitepress = "4710194d-e776-4893-9690-8d956a29c365"
 FreqTables = "da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1"
 Gadfly = "c91e804a-d5a3-530f-b6f0-dfbca275c004"
 MixedModels = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
diff --git a/docs/make.jl b/docs/make.jl
index fb4f8e36f..508852378 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -2,23 +2,31 @@ using Documenter
 using MixedModels
 using StatsAPI
 using StatsBase
+using DocumenterVitepress
 
 makedocs(;
     sitename="MixedModels",
     doctest=true,
     pages=[
         "index.md",
-        "constructors.md",
-        "optimization.md",
-        "GaussHermite.md",
-        "prediction.md",
-        "bootstrap.md",
-        "rankdeficiency.md",
-        "mime.md",
-        "api.md",
+        "Articles" => [
+            "constructors.md",
+            "optimization.md",
+            "GaussHermite.md",
+            "prediction.md",
+            "bootstrap.md",
+            "rankdeficiency.md",
+            "mime.md"
+        ],
+        "api.md"
     ],
+    format = DocumenterVitepress.MarkdownVitepress(
+        repo = "https://github.com/JuliaStats/MixedModels.jl",
+    ),
+    clean = true
 )
 
 deploydocs(;
-    repo="github.com/JuliaStats/MixedModels.jl.git", push_preview=true, devbranch="main"
+    target = "build",
+    repo="github.com/JuliaStats/MixedModels.jl.git", push_preview=true, devbranch="ahk/doc-vite"
 )
diff --git a/docs/src/optimization.md b/docs/src/optimization.md
index f73a8e887..225917758 100644
--- a/docs/src/optimization.md
+++ b/docs/src/optimization.md
@@ -92,7 +92,7 @@ are sometimes called the *best linear unbiased predictors* or BLUPs of the rando
 Although BLUPs an appealing acronym, I don’t find the term particularly instructive (what is a “linear unbiased predictor” and in what sense are these the “best”?) and prefer the term “conditional modes”, because these are the values of $\bf b$ that maximize the density of the conditional distribution $\mathcal{B} | \mathcal{Y} = {\bf y}$.
 For a linear mixed model, where all the conditional and unconditional distributions are Gaussian, these values are also the *conditional means*.
 
-## Internal structure of $\Lambda_\theta$ and $\bf Z$
+## [Internal structure of $\Lambda_\theta$ and $\bf Z$](@id Internal-structure-of-random-effects-coefficients-and-model-matrix)
 
 In the types of `LinearMixedModel` available through the `MixedModels` package, groups of random effects and the corresponding columns of the model matrix, $\bf Z$, are associated with *random-effects terms* in the model formula.