diff --git a/README.md b/README.md
index 5d2e3a4..abc7c56 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@ Convolutional Neural Network inference library running on CUDA.
 - [x] Sigmoid activation
 - [x] ReLU activation
 - [x] Softmax activation
-- [ ] Load weights from file 
+- [x] Load weights from file 
 
 ## Usage
 
@@ -38,4 +38,71 @@ make
 ```sh
 make test_main
 ./test/test_main
-```
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+```
+
+### Create Layers and Model
+
+```cpp
+CUDANet::Model *model =
+    new CUDANet::Model(inputSize, inputChannels, outputSize);
+
+// Conv2d
+CUDANet::Layers::Conv2d *conv2d = new CUDANet::Layers::Conv2d(
+    inputSize, inputChannels, kernelSize, stride, numFilters,
+    CUDANet::Layers::Padding::VALID,
+    CUDANet::Layers::ActivationType::NONE
+);
+
+if (setWeights) {
+    conv2d->setWeights(getConv1Weights().data());
+}
+model->addLayer("conv1", conv2d);
+```
+
+### Sequential and Functional API
+
+Run prediction by passing the input through the layers in the order they have been added.
+
+```cpp
+std::vector<float> input = {...};
+model->predict(input.data());
+```
+
+If you want to use more complex forward pass, using `Concat` or `Add` layers, you can subclass the model class and override the default `predict` function
+
+```cpp
+class MyModel : public CUDANet::Model {
+    ...
+}
+
+...
+
+float* MyModel::predict(const float* input) {
+    float* d_input = inputLayer->forward(input);
+
+    d_conv1 = getLayer("conv1")->forward(d_input);
+    d_conv2 = getLayer("conv2")->forward(d_input);
+
+    d_output = concatLayer->forward(d_conv1, d_conv2);
+
+    return outputLayer->forward(d_input);
+}
+```
+
+### Load Pre-trained Weights
+
+CUDANet uses format similar to safetensors to load weights and biases.
+
+```
+[int64 header size, header, tensor values]
+```
+
+where `header` is a csv format
+
+```
+<tensor_name>,<tensor_size>,<tensor_offset>
+```
+
+To load weights call `load_weights` function on Model object.
+
+To export weights from pytorch you can use `tools/export_model_weights.py` script
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