diff --git a/README.md b/README.md
index ab1de60..90f98cf 100644
--- a/README.md
+++ b/README.md
@@ -33,7 +33,7 @@ cargo add --git https://github.com/EricLBuehler/candle-lora.git candle-lora cand
 `candle-lora-macro` makes using `candle-lora` as simple as adding 2 macros to your model structs and calling a method!
 
 It is inspired by the simplicity of the Python `peft` library's `get_peft_model` method. 
-Together, these macros mean that `candle-lora` can be added to any `candle` model with minimal code changes! To see an example of the benefits, compare the example below (or [here](candle-lora-examples/examples/linear_macro.rs)) to [this](candle-lora-examples/examples/linear.rs), equivalent example. See a precise diff [here](candle-lora-examples/examples/macro_diff.txt).
+Together, these macros mean that `candle-lora` can be added to any `candle` model with minimal code changes!
 
 ## LoRA transformers
 See transformers from Candle which have LoRA integrated [here](candle-lora-transformers/examples/). Currently, the following
diff --git a/candle-lora-examples/examples/macro_diff.txt b/candle-lora-examples/examples/macro_diff.txt
deleted file mode 100644
index f6efb6e..0000000
--- a/candle-lora-examples/examples/macro_diff.txt
+++ /dev/null
@@ -1,80 +0,0 @@
---- examples/linear.rs	2023-09-17 17:20:10.914252466 -0400
-+++ examples/linear_macro.rs	2023-09-17 17:20:10.914252466 -0400
-@@ -1,19 +1,12 @@
--use std::{collections::HashMap, hash::Hash};
-+use candle_core::{DType, Device, Module, Result, Tensor};
-+use candle_lora::{LinearLayerLike, LoraConfig, LoraLinearConfig};
-+use candle_lora_macro::{replace_layer_fields, AutoLoraConvert};
-+use candle_nn::{init, Linear, VarBuilder, VarMap};
- 
--use candle_core::{DType, Device, Result, Tensor};
--use candle_lora::{
--    LinearLayerLike, Lora, LoraConfig, LoraLinearConfig, NewLayers, SelectedLayersBuilder,
--};
--use candle_nn::{init, Linear, Module, VarBuilder, VarMap};
--
--#[derive(PartialEq, Eq, Hash)]
--enum ModelLayers {
--    Layer,
--}
--
--#[derive(Debug)]
-+#[replace_layer_fields]
-+#[derive(AutoLoraConvert, Debug)]
- struct Model {
--    layer: Box<dyn LinearLayerLike>,
-+    layer: Linear,
- }
- 
- impl Module for Model {
-@@ -22,16 +15,6 @@
-     }
- }
- 
--impl Model {
--    fn insert_new(&mut self, new: NewLayers<ModelLayers>) {
--        for (name, linear) in new.linear {
--            match name {
--                ModelLayers::Layer => self.layer = Box::new(linear),
--            }
--        }
--    }
--}
--
- fn main() {
-     let device = Device::Cpu;
-     let dtype = DType::F32;
-@@ -51,23 +34,21 @@
-         layer: Box::new(Linear::new(layer_weight.clone(), None)),
-     };
- 
--    let mut linear_layers = HashMap::new();
--    linear_layers.insert(ModelLayers::Layer, &*model.layer);
--    let selected = SelectedLayersBuilder::new()
--        .add_linear_layers(linear_layers, LoraLinearConfig::new(10, 10))
--        .build();
--
-     let varmap = VarMap::new();
-     let vb = VarBuilder::from_varmap(&varmap, dtype, &device);
- 
-     let loraconfig = LoraConfig::new(1, 1., None);
--
--    let new_layers = Lora::convert_model(selected, loraconfig, &vb);
--
--    model.insert_new(new_layers);
-+    model.get_lora_model(
-+        loraconfig,
-+        &vb,
-+        Some(LoraLinearConfig::new(10, 10)),
-+        None,
-+        None,
-+        None,
-+    );
- 
-     let dummy_image = Tensor::zeros((10, 10), DType::F32, &device).unwrap();
- 
--    let lora_output = model.forward(&dummy_image).unwrap();
--    println!("Output: {lora_output:?}");
-+    let digit = model.forward(&dummy_image).unwrap();
-+    println!("Output: {digit:?}");
- }