diff --git a/tests/brevitas_examples/llm.py b/tests/brevitas_examples/llm.py
index 675bf2df2..a05a2e2c5 100644
--- a/tests/brevitas_examples/llm.py
+++ b/tests/brevitas_examples/llm.py
@@ -4,10 +4,13 @@
 from argparse import Namespace
 from dataclasses import dataclass
 import logging
+import os
 import shutil
 
 import numpy as np
+import onnx
 import pytest
+import torch
 
 from brevitas_examples.llm.main import main
 from brevitas_examples.llm.main import parse_args
@@ -87,6 +90,7 @@ def default_run_args(request):
     args.dataset = "c4"
     args.eval = True
     #args.checkpoint = ptid2pathname(request.node.nodeid) + ".pth" # Example filename which won't clash
+    args.export_prefix = ptid2pathname(request.node.nodeid)
     args.weight_bit_width = 8
     args.weight_quant_granularity = "per_channel"  # "per_tensor", "per_channel", "per_group".
     args.input_bit_width = 8
@@ -284,3 +288,60 @@ def test_small_models_quant_layer(caplog, layer_args):
     args, exp_layer_types = layer_args
     float_ppl, quant_ppl, model = main(args)
     assert_layer_types(model, exp_layer_types)
+
+
+@pytest.fixture(
+    params=[
+        {
+            "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
+            "quantize_weight_zero_point": True,
+            "quantize_input_zero_point": True,
+            "export_target": "onnx_qcdq",},
+        {
+            "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
+            "weight_quant_type": "sym",
+            "input_quant_type": "sym",
+            "export_target": "onnx_qcdq",},])
+def onnx_export_args(default_run_args, request):
+    args = default_run_args
+    export_dict = request.param
+    args.update(**export_dict)
+    yield args
+
+
+def test_small_models_onnx_export(caplog, onnx_export_args):
+    caplog.set_level(logging.INFO)
+    args = onnx_export_args
+    float_ppl, quant_ppl, model = main(args)
+    onnx_model = onnx.load(os.path.join(args.export_prefix, "model.onnx"))
+    shutil.rmtree(args.export_prefix)
+
+
+@pytest.fixture(
+    params=[
+        {
+            "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
+            "eval": False,
+            "quantize_weight_zero_point": True,
+            "quantize_input_zero_point": True,
+            "export_target": "torch_qcdq",},
+        {
+            "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
+            "eval": False,
+            "weight_quant_type": "sym",
+            "input_quant_type": "sym",
+            "export_target": "torch_qcdq",},])
+def torch_export_args(default_run_args, request):
+    args = default_run_args
+    export_dict = request.param
+    args.update(**export_dict)
+    yield args
+
+
+def test_small_models_torch_export(caplog, torch_export_args):
+    caplog.set_level(logging.INFO)
+    args = torch_export_args
+    float_ppl, quant_ppl, model = main(args)
+    filepath = args.export_prefix + ".pt"
+    torchscript_model = torch.jit.load(filepath)
+    os.remove(filepath)