diff --git a/tests/brevitas_finn/brevitas/test_brevitas_avg_pool_export.py b/tests/brevitas_finn/brevitas/test_brevitas_avg_pool_export.py
index 090fc5345..c5bff8c57 100644
--- a/tests/brevitas_finn/brevitas/test_brevitas_avg_pool_export.py
+++ b/tests/brevitas_finn/brevitas/test_brevitas_avg_pool_export.py
@@ -13,7 +13,7 @@
 from qonnx.util.basic import gen_finn_dt_tensor
 import torch
 
-from brevitas.export import FINNManager
+from brevitas.export import export_qonnx
 from brevitas.nn import TruncAvgPool2d
 from brevitas.quant_tensor import QuantTensor
 
@@ -50,7 +50,7 @@ def test_brevitas_avg_pool_export(
     # export
     test_id = request.node.callspec.id
     export_path = test_id + '_' + export_onnx_path
-    FINNManager.export(quant_avgpool, export_path=export_path, input_t=input_quant_tensor)
+    export_qonnx(quant_avgpool, export_path=export_path, input_t=input_quant_tensor)
     model = ModelWrapper(export_path)
     model = model.transform(InferShapes())
     model = model.transform(InferDataTypes())
diff --git a/tests/brevitas_finn/brevitas/test_wbiol.py b/tests/brevitas_finn/brevitas/test_wbiol.py
index 9f41d9255..1b0341bd4 100644
--- a/tests/brevitas_finn/brevitas/test_wbiol.py
+++ b/tests/brevitas_finn/brevitas/test_wbiol.py
@@ -15,6 +15,7 @@
 from brevitas.nn import QuantLinear
 import brevitas.onnx as bo
 from brevitas.quant import Int16Bias
+from brevitas.quant.scaled_int import Int8ActPerTensorFloat
 
 
 @pytest.mark.parametrize("bias", [True, False])
@@ -25,10 +26,10 @@
 @pytest.mark.parametrize("channel_scaling", [True, False])
 @pytest.mark.parametrize("i_bits", [2, 4])
 def test_quant_linear(bias, bias_quant, out_features, in_features, w_bits, channel_scaling, i_bits):
-    # required to generated quantized inputs, not part of the exported model to test
-    quant_inp = QuantIdentity(bit_width=i_bits, return_quant_tensor=True)
-    inp_tensor = quant_inp(torch.randn(1, in_features))
+    inp_tensor = torch.randn(1, in_features)
     linear = QuantLinear(
+        input_quant=Int8ActPerTensorFloat,
+        input_bit_width=i_bits,
         out_features=out_features,
         in_features=in_features,
         bias=bias,
@@ -36,12 +37,10 @@ def test_quant_linear(bias, bias_quant, out_features, in_features, w_bits, chann
         weight_bit_width=w_bits,
         weight_scaling_per_output_channel=channel_scaling)
     linear.eval()
-    model = bo.export_qonnx(linear, input_t=inp_tensor, export_path='linear.onnx')
+    model = bo.export_qonnx(linear, inp_tensor, export_path='linear.onnx')
     model = ModelWrapper(model)
     model = model.transform(InferShapes())
-    # the quantized input tensor passed to FINN should be in integer form
-    int_inp_array = inp_tensor.int(float_datatype=True).detach().numpy()
-    idict = {model.graph.input[0].name: int_inp_array}
+    idict = {model.graph.input[0].name: inp_tensor.detach().numpy()}
     odict = oxe.execute_onnx(model, idict, True)
     produced = odict[model.graph.output[0].name]
     expected = linear(inp_tensor).detach().numpy()
@@ -73,11 +72,11 @@ def test_quant_conv2d(
         padding,
         stride,
         i_bits):
-    # required to generated quantized inputs, not part of the exported model to test
-    quant_inp = QuantIdentity(bit_width=i_bits, return_quant_tensor=True)
-    inp_tensor = quant_inp(torch.randn(1, in_channels, in_features, in_features))
+    inp_tensor = torch.randn(1, in_channels, in_features, in_features)
     try:
         conv = QuantConv2d(
+            input_quant=Int8ActPerTensorFloat,
+            input_bit_width=i_bits,
             in_channels=in_channels,
             #             out_channels=in_channels if dw else out_channels,
             out_channels=
@@ -105,12 +104,11 @@ def test_quant_conv2d(
             assert False
 
     conv.eval()
-    model = bo.export_qonnx(conv, input_t=inp_tensor)
+    model = bo.export_qonnx(conv, inp_tensor)
     model = ModelWrapper(model)
     model = model.transform(InferShapes())
     # the quantized input tensor passed to FINN should be in integer form
-    int_inp_array = inp_tensor.int(float_datatype=True).detach().numpy()
-    idict = {model.graph.input[0].name: int_inp_array}
+    idict = {model.graph.input[0].name: inp_tensor.detach().numpy()}
     odict = oxe.execute_onnx(model, idict, True)
     produced = odict[model.graph.output[0].name]
     expected = conv(inp_tensor).detach().numpy()