diff --git a/python/turbine_models/tests/gen_external_params_test.py b/python/turbine_models/tests/gen_external_params_test.py
new file mode 100644
index 000000000..503a2af6a
--- /dev/null
+++ b/python/turbine_models/tests/gen_external_params_test.py
@@ -0,0 +1,132 @@
+# Copyright 2023 Nod Labs, Inc
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import logging
+from turbine_models.gen_external_params.gen_external_params import quantize
+from turbine_models.model_builder import HFTransformerBuilder
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import unittest
+import os
+import torch
+import pytest
+
+
+class ExternalParamsTest(unittest.TestCase):
+    def testQuantizeF32(self):
+        model_builder = HFTransformerBuilder(
+            example_input=None,
+            hf_id="facebook/opt-350m",
+            auto_model=AutoModelForCausalLM,
+        )
+        model_builder.build_model()
+        quant_weights = quantize(model_builder.model, "", torch.float32)
+        for weight in quant_weights:
+            self.assertNotIn("weight_zp", weight)
+            self.assertNotIn("weight_scale", weight)
+            assert quant_weights[weight].dtype in [torch.float32]
+
+    def testQuantizeF32I8(self):
+        model_builder = HFTransformerBuilder(
+            example_input=None,
+            hf_id="facebook/opt-350m",
+            auto_model=AutoModelForCausalLM,
+        )
+        model_builder.build_model()
+        quant_weights = quantize(model_builder.model, "int8", torch.float32)
+        named_params = dict(model_builder.model.named_parameters())
+        for weight in quant_weights:
+            if "weight_scale" not in weight and "weight_zp" not in weight:
+                if "layers" in weight and "weight" in weight and "norm" not in weight:
+                    assert quant_weights[weight].dtype in [torch.uint8]
+                    assert named_params[weight].size(dim=1) == quant_weights[
+                        weight
+                    ].size(dim=1)
+                else:
+                    assert quant_weights[weight].dtype in [torch.float32]
+            else:
+                assert quant_weights[weight].dtype in [torch.float32]
+
+    def testQuantizeF32I4(self):
+        model_builder = HFTransformerBuilder(
+            example_input=None,
+            hf_id="facebook/opt-350m",
+            auto_model=AutoModelForCausalLM,
+        )
+        model_builder.build_model()
+        quant_weights = quantize(model_builder.model, "int4", torch.float32)
+        named_params = dict(model_builder.model.named_parameters())
+        for weight in quant_weights:
+            if "weight_scale" not in weight and "weight_zp" not in weight:
+                if "layers" in weight and "weight" in weight and "norm" not in weight:
+                    assert quant_weights[weight].dtype in [torch.uint8]
+                    assert named_params[weight].size(dim=1) == 2 * quant_weights[
+                        weight
+                    ].size(dim=1)
+                else:
+                    assert quant_weights[weight].dtype in [torch.float32]
+            else:
+                assert quant_weights[weight].dtype in [torch.float32]
+
+    def testQuantizeF16(self):
+        model_builder = HFTransformerBuilder(
+            example_input=None,
+            hf_id="facebook/opt-350m",
+            auto_model=AutoModelForCausalLM,
+        )
+        model_builder.build_model()
+        quant_weights = quantize(model_builder.model.half(), "", torch.float16)
+        for weight in quant_weights:
+            self.assertNotIn("weight_zp", weight)
+            self.assertNotIn("weight_scale", weight)
+            assert quant_weights[weight].dtype in [torch.float16]
+
+    @pytest.mark.xfail(reason="brevitas issue with f16 int8 quanttensor")
+    def testQuantizeF16I8(self):
+        model_builder = HFTransformerBuilder(
+            example_input=None,
+            hf_id="facebook/opt-350m",
+            auto_model=AutoModelForCausalLM,
+        )
+        model_builder.build_model()
+        quant_weights = quantize(model_builder.model.half(), "int8", torch.float16)
+        named_params = dict(model_builder.model.named_parameters())
+        for weight in quant_weights:
+            if "weight_scale" not in weight and "weight_zp" not in weight:
+                if "layers" in weight and "weight" in weight and "norm" not in weight:
+                    assert quant_weights[weight].dtype in [torch.uint8]
+                    assert named_params[weight].size(dim=1) == quant_weights[
+                        weight
+                    ].size(dim=1)
+                else:
+                    assert quant_weights[weight].dtype in [torch.float16]
+            else:
+                assert quant_weights[weight].dtype in [torch.float16]
+
+    def testQuantizeF16I4(self):
+        model_builder = HFTransformerBuilder(
+            example_input=None,
+            hf_id="facebook/opt-350m",
+            auto_model=AutoModelForCausalLM,
+        )
+        model_builder.build_model()
+        quant_weights = quantize(model_builder.model.half(), "int4", torch.float16)
+        named_params = dict(model_builder.model.named_parameters())
+        for weight in quant_weights:
+            if "weight_scale" not in weight and "weight_zp" not in weight:
+                if "layers" in weight and "weight" in weight and "norm" not in weight:
+                    assert quant_weights[weight].dtype in [torch.uint8]
+                    assert named_params[weight].size(dim=1) == 2 * quant_weights[
+                        weight
+                    ].size(dim=1)
+                else:
+                    assert quant_weights[weight].dtype in [torch.float16]
+            else:
+                assert quant_weights[weight].dtype in [torch.float16]
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.DEBUG)
+    unittest.main()