diff --git a/candle-examples/examples/mnist-training/main.rs b/candle-examples/examples/mnist-training/main.rs
index d50cb944..937510c7 100644
--- a/candle-examples/examples/mnist-training/main.rs
+++ b/candle-examples/examples/mnist-training/main.rs
@@ -163,11 +163,16 @@ impl Model for Mlp {
     }
 }
 
-fn training_loop<M: Model>(
-    m: candle_nn::vision::Dataset,
+struct TrainingArgs {
     learning_rate: f64,
     load: Option<String>,
     save: Option<String>,
+    epochs: usize,
+}
+
+fn training_loop<M: Model>(
+    m: candle_nn::vision::Dataset,
+    args: &TrainingArgs,
 ) -> anyhow::Result<()> {
     let dev = candle::Device::cuda_if_available(0)?;
 
@@ -181,17 +186,17 @@ fn training_loop<M: Model>(
     let mut vs = VarStore::new(DType::F32, dev.clone());
     let model = M::new(vs.clone())?;
 
-    if let Some(load) = load {
+    if let Some(load) = &args.load {
         println!("loading weights from {load}");
-        vs.load(&load)?
+        vs.load(load)?
     }
 
     let all_vars = vs.all_vars();
     let all_vars = all_vars.iter().collect::<Vec<_>>();
-    let sgd = candle_nn::SGD::new(&all_vars, learning_rate);
+    let sgd = candle_nn::SGD::new(&all_vars, args.learning_rate);
     let test_images = m.test_images.to_device(&dev)?;
     let test_labels = m.test_labels.to_dtype(DType::U32)?.to_device(&dev)?;
-    for epoch in 1..200 {
+    for epoch in 1..args.epochs {
         let logits = model.forward(&train_images)?;
         let log_sm = ops::log_softmax(&logits, D::Minus1)?;
         let loss = loss::nll(&log_sm, &train_labels)?;
@@ -211,9 +216,9 @@ fn training_loop<M: Model>(
             100. * test_accuracy
         );
     }
-    if let Some(save) = save {
+    if let Some(save) = &args.save {
         println!("saving trained weights in {save}");
-        vs.save(&save)?
+        vs.save(save)?
     }
     Ok(())
 }
@@ -232,6 +237,9 @@ struct Args {
     #[arg(long)]
     learning_rate: Option<f64>,
 
+    #[arg(long, default_value_t = 200)]
+    epochs: usize,
+
     /// The file where to save the trained weights, in safetensors format.
     #[arg(long)]
     save: Option<String>,
@@ -250,12 +258,18 @@ pub fn main() -> anyhow::Result<()> {
     println!("test-images: {:?}", m.test_images.shape());
     println!("test-labels: {:?}", m.test_labels.shape());
 
+    let default_learning_rate = match args.model {
+        WhichModel::Linear => 1.,
+        WhichModel::Mlp => 0.05,
+    };
+    let training_args = TrainingArgs {
+        epochs: args.epochs,
+        learning_rate: args.learning_rate.unwrap_or(default_learning_rate),
+        load: args.load,
+        save: args.save,
+    };
     match args.model {
-        WhichModel::Linear => {
-            training_loop::<LinearModel>(m, args.learning_rate.unwrap_or(1.), args.load, args.save)
-        }
-        WhichModel::Mlp => {
-            training_loop::<Mlp>(m, args.learning_rate.unwrap_or(0.01), args.load, args.save)
-        }
+        WhichModel::Linear => training_loop::<LinearModel>(m, &training_args),
+        WhichModel::Mlp => training_loop::<Mlp>(m, &training_args),
     }
 }