diff --git a/candle-nn/src/var_builder.rs b/candle-nn/src/var_builder.rs
index 03929681..bf5d5b43 100644
--- a/candle-nn/src/var_builder.rs
+++ b/candle-nn/src/var_builder.rs
@@ -1,3 +1,6 @@
+//! A `VarBuilder` is used to retrieve variables used by a model. These variables can either come
+//! from a pre-trained checkpoint, e.g. using `VarBuilder::from_safetensors`, or initialized for
+//! training, e.g. using `VarBuilder::from_varmap`.
 use crate::VarMap;
 use candle::{safetensors::Load, DType, Device, Error, Result, Shape, Tensor};
 use safetensors::{slice::IndexOp, tensor::SafeTensors};
@@ -107,6 +110,15 @@ impl<'a, B: Backend> VarBuilderArgs<'a, B> {
         self.path.join(".")
     }
 
+    /// Returns a new `VarBuilder` using the root path.
+    pub fn root(&self) -> Self {
+        Self {
+            data: self.data.clone(),
+            path: vec![],
+            _phantom: std::marker::PhantomData,
+        }
+    }
+
     /// Returns a new `VarBuilder` with the prefix set to `prefix`.
     pub fn set_prefix(&self, prefix: impl ToString) -> Self {
         Self {
@@ -327,18 +339,29 @@ impl<'a> VarBuilder<'a> {
         }
     }
 
+    /// Initializes a `VarBuilder` that uses zeros for any tensor.
     pub fn zeros(dtype: DType, dev: &Device) -> Self {
         Self::new(Box::new(Zeros), dtype, dev.clone())
     }
 
+    /// Initializes a `VarBuilder` that retrieves tensors stored in a hashtable. An error is
+    /// returned if no tensor is available under the requested path or on shape mismatches.
     pub fn from_tensors(ts: HashMap<String, Tensor>, dtype: DType, dev: &Device) -> Self {
         Self::new(Box::new(ts), dtype, dev.clone())
     }
 
+    /// Initializes a `VarBuilder` using a `VarMap`. The requested tensors are created and
+    /// initialized on new paths, the same tensor is used if the same path is requested multiple
+    /// times. This is commonly used when initializing a model before training.
+    ///
+    /// Note that it is possible to load the tensor values after model creation using the `load`
+    /// method on `varmap`, this can be used to start model training from an existing checkpoint.
     pub fn from_varmap(varmap: &VarMap, dtype: DType, dev: &Device) -> Self {
         Self::new(Box::new(varmap.clone()), dtype, dev.clone())
     }
 
+    /// Initializes a `VarBuilder` that retrieves tensors stored in a collection of safetensors
+    /// files.
     pub fn from_safetensors(safetensors: Vec<SafeTensors<'a>>, dtype: DType, dev: &Device) -> Self {
         let mut routing = HashMap::new();
         for (index, sf) in safetensors.iter().enumerate() {
@@ -353,6 +376,7 @@ impl<'a> VarBuilder<'a> {
         Self::new(Box::new(tensors), dtype, dev.clone())
     }
 
+    /// Initializes a `VarBuilder` that retrieves tensors stored in a numpy npz file.
     pub fn from_npz<P: AsRef<std::path::Path>>(p: P, dtype: DType, dev: &Device) -> Result<Self> {
         let npz = candle::npy::NpzTensors::new(p)?;
         Ok(Self::new(Box::new(npz), dtype, dev.clone()))