diff --git a/examples/convnext/convnext.chpl b/examples/convnext/convnext.chpl
new file mode 100644
index 000000000..ed1a7c6e3
--- /dev/null
+++ b/examples/convnext/convnext.chpl
@@ -0,0 +1,114 @@
+module ConvNext {
+  use Tensor;
+  use Network;
+  use List;
+
+  class LayerNorm2d {
+
+  }
+
+  class ConvNextStem {
+    var conv: owned Conv2D(eltType);
+    var layernorm_2d: owned LayerNorm2d(eltType);
+
+    proc init(in_channels: int, out_channels: int, kernel_size: int = 3) {
+      super.init(eltType);
+      this.conv = new Conv2D(eltType, channels=in_channels, features=out_channels, kernel=kernel_size, stride=kernel_size);
+      this.layernorm_2d = new LayerNorm2d(); // incomplete
+    }
+    
+    override proc forward(x: Tensor(eltType)): Tensor(eltType) {
+      x = this.conv(x)
+      x = this.layernorm_2d(x) // incomplete
+      return x
+    }
+  }
+
+  class ConvNextBlock: Module(?) {
+    var depthwise_conv: owned Conv2D(eltType); // need groups param in convolutions
+    var layernorm_2d: owned LayerNorm2d(eltType);
+    var conv1: owned Conv2D(eltType);
+    // var gelu; // incomplete, need class wrappers for activation functions?
+    var conv2: owned Conv2D(eltType);
+    var layer_scale: owned Parameter(eltType);
+
+    proc init(in_channels: int, layer_scale: int = 1e-6, kernel_size: int = 7, stochastic_depth_prob:int = 1) {
+
+      /////////////////// need to finish this
+      super.init(eltType);
+      this.depthwise_conv = new Conv2D();
+      this.conv1 = new Conv2D();
+      // this.gelu 
+      this.conv2 = new Conv2D();
+      this.layer_scale = new Parameter();
+    }
+
+    override proc forward(x: Tensor(eltType)): Tensor(eltType) {
+      residual = x;
+      x = this.depthwise_conv(x);
+      x = this.layernorm_2d(x);
+      x = this.conv1(x);
+      x = x.gelu(); // should be changed to use classes
+      x = this.conv2(x);
+      x = this.layer_scale(x);
+      x = residual + x;
+      return x;
+    }
+  }
+
+  class ConvNextDownsample {
+    var layernorm_2d: owned LayerNorm2d(eltType);
+    var conv: owned Conv2D(eltType);
+
+    proc init(d_in: int, d_out: int, width:int = 2) {
+      super.init(eltType)
+      this.layer_norm = new LayerNorm2d(); // incomplete
+      this.conv = new Conv2D(eltType, channels=d_in, features=d_out, kernel=width, stride=width);
+    }
+
+    override proc forward(x: Tensor(eltType)): Tensor(eltType) {
+      x = this.layernorm_2d(x);
+      x = this.conv(x);
+      return x;
+    }
+  }
+
+  class ConvNextClassifier {
+    // var global_pool: // incomplete, need AdaptiveAvgPool2d (Global average pooling)
+    // adaptiveAvgPool2d is a method on NDArrays
+    // var flatten: // need Flatten
+    // var layer_norm: LayerNorm();
+    // var linear: Linear();
+
+    proc init(d_in: int, d_out: int) {
+      // this.global_pool = AdaptiveAvgPool2d(...);
+      // this.flatten = Flatten();
+      // this.layer_norm = LayerNorm(...);
+      // this.linear = Linear();
+    }
+
+    override proc forward(x: Tensor(eltType)): Tensor(eltType) {
+      x = this.global_pool(x);
+      x = this.flatten(x);
+      x = this.layer_norm(x);
+      x = this.linear(x);
+      return x;
+    }
+  }
+
+  class ConvNext {
+    var layers: owned Sequential(eltType);
+
+    proc init(in_channels: int, out_channels: int, blocks: [] int = [96]) {
+      var layer_list = list(owned Module(eltType));
+      layer_list.pushBack()// etc
+
+
+      this.layers = new Sequential(layer_list);
+    }
+
+    override proc forward(x: Tensor(eltType)): Tensor(eltType) {
+      x = layers(x);
+    }
+  }
+}
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