diff --git a/src/lc0ctl/leela2onnx.cc b/src/lc0ctl/leela2onnx.cc
index 107131ae06..5336aa08bf 100644
--- a/src/lc0ctl/leela2onnx.cc
+++ b/src/lc0ctl/leela2onnx.cc
@@ -53,6 +53,8 @@ const OptionId kOutputValue{
     "ONNX name to use for value policy head output node."};
 const OptionId kOutputMlh{"mlh-head-name", "MlhHeadName",
                           "ONNX name to use for the MLH head output node."};
+const OptionId kOnnxToPytorch{"onnx2pytorch", "Onnx2Pytorch",
+                          "Only use layer definitions supported by onnx2pytorch."};
 
 bool ProcessParameters(OptionsParser* options) {
   options->Add<StringOption>(kInputFilenameId);
@@ -63,6 +65,7 @@ bool ProcessParameters(OptionsParser* options) {
   options->Add<StringOption>(kOutputWdl) = "/output/wdl";
   options->Add<StringOption>(kOutputValue) = "/output/value";
   options->Add<StringOption>(kOutputMlh) = "/output/mlh";
+  options->Add<BoolOption>(kOnnxToPytorch) = false;
   if (!options->ProcessAllFlags()) return false;
 
   const OptionsDict& dict = options->GetOptionsDict();
@@ -94,6 +97,9 @@ void ConvertLeelaToOnnx() {
     onnx_options.output_wdl = dict.Get<std::string>(kOutputWdl);
     onnx_options.output_value = dict.Get<std::string>(kOutputValue);
     onnx_options.output_wdl = dict.Get<std::string>(kOutputWdl);
+    // onnx2pytorch only needs an alternate layernorm-implementation, so it's currently
+    // only enables that. Might need to be extended in the future.
+    onnx_options.alternative_layer_normalization = dict.Get<bool>(kOnnxToPytorch);
     weights_file = ConvertWeightsToOnnx(weights_file, onnx_options);
   }
 
diff --git a/src/neural/onnx/builder.cc b/src/neural/onnx/builder.cc
index 7dcb04c19a..fb54f0f698 100644
--- a/src/neural/onnx/builder.cc
+++ b/src/neural/onnx/builder.cc
@@ -137,10 +137,12 @@ std::string OnnxBuilder::Conv(const std::string& name,
                               const OnnxConst& kernel_weights,
                               const OnnxConst& bias_weights, int pads) {
   auto* node = model_.mutable_graph()->add_node();
+  auto shape = kernel_weights.GetDimensions().back();
   auto out = PopulateStdNodeFields(node, name, input_name, "Conv");
   node->add_input(AddInitializer(name + "/w/kernel", kernel_weights));
   node->add_input(AddInitializer(name + "/w/bias", bias_weights));
   AddIntsAttribute(node, "pads", {pads, pads, pads, pads});
+  AddIntsAttribute(node, "kernel_shape", {shape, shape});
   return out;
 }
 
@@ -438,4 +440,40 @@ std::string OnnxBuilder::Mish(const std::string& name,
   return PopulateStdNodeFields(node, name, input, "Mish");
 }
 
+std::string OnnxBuilder::Sqrt(const std::string& name,
+                              const std::string& input) {
+  auto* node = model_.mutable_graph()->add_node();
+  return PopulateStdNodeFields(node, name, input, "Sqrt");
+}
+
+std::string OnnxBuilder::Reciprocal(const std::string& name,
+                                    const std::string& input) {
+  auto* node = model_.mutable_graph()->add_node();
+  return PopulateStdNodeFields(node, name, input, "Reciprocal");
+}
+
+std::string OnnxBuilder::Cast(const std::string& name, const std::string& input,
+                              pblczero::TensorProto::DataType type) {
+  auto* node = model_.mutable_graph()->add_node();
+  auto out = PopulateStdNodeFields(node, name, input, "Cast");
+  AddIntAttribute(node, "to", type);
+  return out;
+}
+
+std::string OnnxBuilder::ReduceMean(const std::string& name,
+                                    const std::string& input,
+                                    std::initializer_list<int> axes) {
+  auto* node = model_.mutable_graph()->add_node();
+  auto out = PopulateStdNodeFields(node, name, input, "ReduceMean");
+  if (opset_ < 18) {
+    AddIntsAttribute(node, "axes", axes);
+  } else {
+    node->add_input(AddInitializer(
+        name + "/axes",
+        Int64OnnxConst(std::vector<int64_t>(begin(axes), end(axes)),
+                       {static_cast<int>(axes.size())})));
+  }
+  return out;
+}
+
 }  // namespace lczero
diff --git a/src/neural/onnx/builder.h b/src/neural/onnx/builder.h
index c1f6c5e957..ba0d3705a5 100644
--- a/src/neural/onnx/builder.h
+++ b/src/neural/onnx/builder.h
@@ -65,7 +65,7 @@ class OnnxBuilder {
   std::string Add(const std::string& name, const std::string& input1,
                   const std::string& input2);
   std::string Add(const std::string& name, const std::string& input1,
-                  const OnnxConst&);
+                  const OnnxConst& input2);
   std::string GlobalAveragePool(const std::string& name,
                                 const std::string& input);
   std::string Squeeze(const std::string& name, const std::string& input,
@@ -120,6 +120,12 @@ class OnnxBuilder {
   std::string Where(const std::string& name, const std::string& input1,
                     const std::string& input2, const std::string& input3);
   std::string Mish(const std::string& name, const std::string& input);
+  std::string Sqrt(const std::string& name, const std::string& input);
+  std::string Reciprocal(const std::string& name, const std::string& input);
+  std::string Cast(const std::string& name, const std::string& input,
+                   pblczero::TensorProto::DataType type);
+  std::string ReduceMean(const std::string& name, const std::string& input,
+                         std::initializer_list<int> axes);
   // Returns ONNX model as protobuf.
   const pblczero::ModelProto& as_proto() const { return model_; }
   // Returns serialized model.
diff --git a/src/neural/onnx/converter.cc b/src/neural/onnx/converter.cc
index 59297b3ead..6c2869a81c 100644
--- a/src/neural/onnx/converter.cc
+++ b/src/neural/onnx/converter.cc
@@ -111,6 +111,11 @@ class Converter {
                           const std::string& encoder_in,
                           const std::string& name);
 
+  std::string MakeLayerNorm(OnnxBuilder* builder, const std::string& input,
+                            const std::string& name,
+                            const lczero::OnnxConst& gammas,
+                            const lczero::OnnxConst& betas, float eps = 1e-6);
+
   std::string MakeEncoderLayer(OnnxBuilder* builder,
                                const LegacyWeights::EncoderLayer& layer,
                                int embedding_size, int heads,
@@ -320,10 +325,10 @@ std::string Converter::MakeSmolgen(OnnxBuilder* builder,
       name + "/smolgen/dense1/b", flow,
       *GetWeghtsConverter(layer.mha.smolgen.dense1_b, {smolgen_hidden_sz}));
   flow = MakeActivation(builder, flow, name + "/smolgen/dense1", activation);
-  flow = builder->LayerNormalization(
-      name + "/smolgen/ln1", flow,
+  flow = MakeLayerNorm(
+      builder, flow, name + "/smolgen/ln1",
       *GetWeghtsConverter(layer.mha.smolgen.ln1_gammas, {smolgen_hidden_sz}),
-      *GetWeghtsConverter(layer.mha.smolgen.ln1_betas, {smolgen_hidden_sz}), 1,
+      *GetWeghtsConverter(layer.mha.smolgen.ln1_betas, {smolgen_hidden_sz}),
       1e-3);
   flow = builder->MatMul(
       name + "/smolgen/dense2/w", flow,
@@ -333,13 +338,12 @@ std::string Converter::MakeSmolgen(OnnxBuilder* builder,
                       *GetWeghtsConverter(layer.mha.smolgen.dense2_b,
                                           {smolgen_gen_sz * heads}));
   flow = MakeActivation(builder, flow, name + "/smolgen/dense2", activation);
-  flow = builder->LayerNormalization(
-      name + "/smolgen/ln2", flow,
-      *GetWeghtsConverter(layer.mha.smolgen.ln2_gammas,
-                          {smolgen_gen_sz * heads}),
-      *GetWeghtsConverter(layer.mha.smolgen.ln2_betas,
-                          {smolgen_gen_sz * heads}),
-      1, 1e-3);
+  flow = MakeLayerNorm(builder, flow, name + "/smolgen/ln2",
+                       *GetWeghtsConverter(layer.mha.smolgen.ln2_gammas,
+                                           {smolgen_gen_sz * heads}),
+                       *GetWeghtsConverter(layer.mha.smolgen.ln2_betas,
+                                           {smolgen_gen_sz * heads}),
+                       1e-3);
   flow =
       builder->Reshape(name + "/smolgen/gen_from/reshape", flow,
                        builder->AddInitializer(
@@ -354,6 +358,33 @@ std::string Converter::MakeSmolgen(OnnxBuilder* builder,
   return flow;
 }
 
+std::string Converter::MakeLayerNorm(OnnxBuilder* builder,
+                                     const std::string& input,
+                                     const std::string& name,
+                                     const lczero::OnnxConst& gammas,
+                                     const lczero::OnnxConst& betas,
+                                     float eps) {
+  if (!options_.alternative_layer_normalization) {
+    return builder->LayerNormalization(name, input, gammas, betas, 1, eps);
+  }
+  auto in =
+      builder->Cast(name + "/to_float", input, pblczero::TensorProto::FLOAT);
+  auto flow = builder->ReduceMean(name + "/mean", in, {1});
+  in = builder->Sub(name + "/centered", in, flow);
+  flow = builder->Mul(name + "/squared", in, in);
+  flow = builder->ReduceMean(name + "/var", flow, {1});
+  flow =
+      builder->Add(name + "/var_eps", flow,
+                   static_cast<const OnnxConst&>(FloatOnnxConst({eps}, {1})));
+  flow = builder->Sqrt(name + "/std", flow);
+  flow = builder->Reciprocal(name + "/inv_std", flow);
+  flow = builder->Mul(name + "/normalized", in, flow);
+  flow = builder->Cast(name + "/to_data_type", flow, GetDataType());
+  flow = builder->Mul(name + "/gammas", flow, gammas);
+  flow = builder->Add(name + "/betas", flow, betas);
+  return flow;
+}
+
 std::string Converter::MakeEncoderLayer(
     OnnxBuilder* builder, const LegacyWeights::EncoderLayer& layer,
     int embedding_size, int heads, const std::string& encoder_in,
@@ -430,11 +461,10 @@ std::string Converter::MakeEncoderLayer(
     alpha_in = encoder_in;
   }
   flow = builder->Add(name + "/mha/out/skip", flow, alpha_in);
-
-  auto ffn_in = builder->LayerNormalization(
-      name + "/ln1", flow,
-      *GetWeghtsConverter(layer.ln1_gammas, {embedding_size}),
-      *GetWeghtsConverter(layer.ln1_betas, {embedding_size}), 1);
+  auto ffn_in =
+      MakeLayerNorm(builder, flow, name + "/ln1",
+                    *GetWeghtsConverter(layer.ln1_gammas, {embedding_size}),
+                    *GetWeghtsConverter(layer.ln1_betas, {embedding_size}));
   const int dff_size = layer.ffn.dense1_b.size();
   flow =
       builder->MatMul(name + "/ffn/dense1/w", ffn_in,
@@ -462,10 +492,9 @@ std::string Converter::MakeEncoderLayer(
     alpha_ffn_in = ffn_in;
   }
   flow = builder->Add(name + "/ffn/skip", flow, alpha_ffn_in);
-  flow = builder->LayerNormalization(
-      name + "/ln2", flow,
-      *GetWeghtsConverter(layer.ln2_gammas, {embedding_size}),
-      *GetWeghtsConverter(layer.ln2_betas, {embedding_size}), 1);
+  flow = MakeLayerNorm(builder, flow, name + "/ln2",
+                       *GetWeghtsConverter(layer.ln2_gammas, {embedding_size}),
+                       *GetWeghtsConverter(layer.ln2_betas, {embedding_size}));
   return flow;
 }
 
diff --git a/src/neural/onnx/converter.h b/src/neural/onnx/converter.h
index b906aebfce..f397014324 100644
--- a/src/neural/onnx/converter.h
+++ b/src/neural/onnx/converter.h
@@ -44,6 +44,7 @@ struct WeightsToOnnxConverterOptions {
   int batch_size = -1;
   int opset = 17;
   bool alt_mish = false;
+  bool alternative_layer_normalization = false;
 };
 
 // Converts "classical" weights file to weights file with embedded ONNX model.
diff --git a/src/neural/onnx/network_onnx.cc b/src/neural/onnx/network_onnx.cc
index e5eb8e0443..ef3b0b33af 100644
--- a/src/neural/onnx/network_onnx.cc
+++ b/src/neural/onnx/network_onnx.cc
@@ -446,6 +446,8 @@ std::unique_ptr<Network> MakeOnnxNetwork(const std::optional<WeightsFile>& w,
     converter_options.opset = opts.GetOrDefault<int>("opset", 17);
     converter_options.alt_mish = opts.GetOrDefault<bool>(
         "alt_mish", kProvider == OnnxProvider::CPU ? true : false);
+    converter_options.alternative_layer_normalization =
+        opts.GetOrDefault<bool>("alternative_layer_normalization", true);
     converter_options.data_type_ =
         fp16 ? WeightsToOnnxConverterOptions::DataType::kFloat16
              : WeightsToOnnxConverterOptions::DataType::kFloat32;