diff --git a/tests/brevitas_examples/test_llm.py b/tests/brevitas_examples/test_llm.py
index dc5d42a03..1143186f1 100644
--- a/tests/brevitas_examples/test_llm.py
+++ b/tests/brevitas_examples/test_llm.py
@@ -2,7 +2,6 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from argparse import Namespace
-from collections import defaultdict
 from dataclasses import dataclass
 import logging
 import os
@@ -83,7 +82,7 @@ def assert_layer_types_count(model, exp_layer_types_count):
 
     for name, count in exp_layer_types_count.items():
         curr_count = 0 if name not in layer_types_count else layer_types_count[name]
-        assert count == curr_count, f"Expect {count} instances of layer type: {name}, found {curr_count}."
+        assert count == curr_count, f"Expected {count} instances of layer type: {name}, found {curr_count}."
 
 
 class UpdatableNamespace(Namespace):
@@ -299,9 +298,7 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
         "mistral-fp8_fnuz",
         "llama-mxfp8",
         "llama-int8-act_equalization=layerwise",
-        "mistral-int8-quant-last-layer",
-        "llama-rotation-mixed-fx",
-        "llama-rotation-full-fx",],
+        "mistral-int8-quant-last-layer",],
     params=[
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
@@ -314,12 +311,7 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                     "<class 'brevitas.core.quant.int.RescalingIntQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
                     "<class 'brevitas.core.quant.int.RescalingIntQuant'>",},
-            "exp_layer_types_count": {
-                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
-                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
-                    14,  # Q/K/V/O projs + Up/Gate/Down projs
-                "<class 'brevitas.core.quant.int.RescalingIntQuant'>": 28,
-            }},  # input_quant/weight_quant
+        },  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "input_bit_width": None,
@@ -331,12 +323,7 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                     "<class 'brevitas.proxy.runtime_quant.ActQuantProxyFromInjector'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
                     "<class 'brevitas.core.quant.int.RescalingIntQuant'>",},
-            "exp_layer_types_count": {
-                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
-                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
-                    14,  # Q/K/V/O projs + Up/Gate/Down projs
-                "<class 'brevitas.core.quant.int.RescalingIntQuant'>": 14,
-            }},  # input_quant/weight_quant
+        },  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "weight_quant_format": "float_ocp_e4m3",
@@ -350,11 +337,7 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                     "<class 'brevitas.core.quant.float.FloatQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
                     "<class 'brevitas.core.quant.float.FloatQuant'>",},
-            "exp_layer_types_count": {
-                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
-                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
-                    14,  # Q/K/V/O projs + Up/Gate/Down projs
-                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,}},  # input_quant/weight_quant
+        },  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "weight_quant_format": "float_fnuz_e4m3",
@@ -368,11 +351,7 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                     "<class 'brevitas.core.quant.float.FloatQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
                     "<class 'brevitas.core.quant.float.FloatQuant'>",},
-            "exp_layer_types_count": {
-                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
-                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
-                    14,  # Q/K/V/O projs + Up/Gate/Down projs
-                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,}},  # input_quant/weight_quant
+        },  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
             "weight_quant_format": "float_ocp_e4m3",
@@ -399,7 +378,112 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
                     "<class 'brevitas.core.quant.float.FloatQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant.input_view_impl":
-                    "<class 'brevitas.core.function_wrapper.shape.OverSubChannelBlockView'>",},
+                    "<class 'brevitas.core.function_wrapper.shape.OverSubChannelBlockView'>",},},
+        {
+            "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
+            "act_equalization": "layerwise",
+            "exp_layer_types": {
+                "model.layers.0.self_attn.q_proj":
+                    "<class 'brevitas.nn.equalized_layer.EqualizedModule'>",
+                "model.layers.0.self_attn.q_proj.layer":
+                    "<class 'brevitas.nn.quant_linear.QuantLinear'>",},},
+        {
+            "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
+            "quantize_last_layer": True,
+            "exp_layer_types": {
+                "lm_head": "<class 'brevitas.nn.quant_linear.QuantLinear'>"},},
+    ])  # LM Head + Q/K/V/O projs + Up/Gate/Down projs
+def layer_args(default_run_args, request):
+    args = default_run_args
+    layer_dict = request.param
+    exp_layer_types = layer_dict["exp_layer_types"]
+    del layer_dict["exp_layer_types"]
+    args.update(**layer_dict)
+    yield args, exp_layer_types
+
+
+@pytest.mark.llm
+@requires_pt_ge('2.2')
+def test_small_models_quant_layer(caplog, layer_args):
+    caplog.set_level(logging.INFO)
+    args, exp_layer_types = layer_args
+    if args.replace_rmsnorm:
+        if torch_version < version.parse('2.4'):
+            pytest.skip("Replacing RMSNorm requires torch 2.4+ or greater")
+        if hasattr(args, 'rotation') and args.rotation == 'fx' and platform.system() == 'Windows':
+            pytest.skip("Skipping dynamo + windows")
+    float_ppl, quant_ppl, model = validate_args_and_run_main(args)
+    assert_layer_types(model, exp_layer_types)
+
+
+@pytest_cases.fixture(
+    ids=[
+        "mistral-int8",
+        "mistral-weight-only",
+        "mistral-fp8_ocp",
+        "mistral-fp8_fnuz",
+        "llama-mxfp8",
+        "llama-int8-act_equalization=layerwise",
+        "mistral-int8-quant-last-layer",
+        "llama-rotation-mixed-fx",
+        "llama-rotation-full-fx",],
+    params=[
+        {
+            "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.int.RescalingIntQuant'>": 28,
+            }},  # input_quant/weight_quant
+        {
+            "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
+            "input_bit_width": None,
+            "act_calibration": False,
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.int.RescalingIntQuant'>": 14,
+            }},  # input_quant/weight_quant
+        {
+            "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
+            "weight_quant_format": "float_ocp_e4m3",
+            "weight_quant_type": "sym",
+            "input_quant_format": "float_ocp_e5m2",
+            "input_quant_type": "sym",
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,}},  # input_quant/weight_quant
+        {
+            "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
+            "weight_quant_format": "float_fnuz_e4m3",
+            "weight_quant_type": "sym",
+            "input_quant_format": "float_fnuz_e5m2",
+            "input_quant_type": "sym",
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,}},  # input_quant/weight_quant
+        {
+            "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
+            "weight_quant_format": "float_ocp_e4m3",
+            "weight_scale_precision": "po2_scale",
+            "weight_param_method": "stats",
+            "weight_quant_granularity": "per_group",
+            "weight_group_size": 16,
+            "weight_quant_type": "sym",
+            "input_quant_format": "float_ocp_e5m2",
+            "input_scale_type": "dynamic",
+            "input_scale_precision": "po2_scale",
+            "input_param_method": "stats",
+            "input_quant_granularity": "per_group",
+            "input_group_size": 16,
+            "input_quant_type": "sym",
+            "act_calibration": False,
             "exp_layer_types_count": {
                 "<class 'brevitas.nn.quant_linear.QuantLinear'>":
                     14,  # Q/K/V/O projs + Up/Gate/Down projs
@@ -413,11 +497,6 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
         {
             "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
             "act_equalization": "layerwise",
-            "exp_layer_types": {
-                "model.layers.0.self_attn.q_proj":
-                    "<class 'brevitas.nn.equalized_layer.EqualizedModule'>",
-                "model.layers.0.self_attn.q_proj.layer":
-                    "<class 'brevitas.nn.quant_linear.QuantLinear'>",},
             "exp_layer_types_count": {
                 "<class 'brevitas.nn.quant_linear.QuantLinear'>":
                     14,  # Q/K/V/O projs + Up/Gate/Down projs
@@ -428,8 +507,6 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "quantize_last_layer": True,
-            "exp_layer_types": {
-                "lm_head": "<class 'brevitas.nn.quant_linear.QuantLinear'>"},
             "exp_layer_types_count": {
                 "<class 'brevitas.nn.quant_linear.QuantLinear'>": 15,
             }},  # LM Head + Q/K/V/O projs + Up/Gate/Down projs
@@ -442,11 +519,6 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
             "rotation_orphan_sink": True,
             "convert_layernorm_to_rmsnorm": True,
             "rotation": "fx",
-            "exp_layer_types": {
-                "L__self___model_layers_0_self_attn_k_proj":
-                    "<class 'torch.nn.modules.linear.Linear'>",
-                "L__self___model_layers_0_self_attn_o_proj":
-                    "<class 'brevitas.nn.equalized_layer.RotatedModule'>",},
             "exp_layer_types_count": {
                 "<class 'brevitas.nn.equalized_layer.RotatedModule'>":
                     4,  # Sinks: O proj + Down proj
@@ -463,42 +535,31 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
             "rotation_orphan_sink": False,
             "convert_layernorm_to_rmsnorm": True,
             "rotation": "fx",
-            "exp_layer_types": {
-                "L__self___model_layers_0_self_attn_k_proj":
-                    "<class 'torch.nn.modules.linear.Linear'>",
-                "L__self___model_layers_0_self_attn_o_proj":
-                    "<class 'torch.nn.modules.linear.Linear'>"},
             "exp_layer_types_count": {
                 "<class 'torch.nn.modules.linear.Linear'>":
                     15,  # LM Head + Q/K/V projs + Up/Gate/Down projs
                 "<class 'torch.nn.modules.normalization.RMSNorm'>": 5,  # Input + Post attention
                 "<class 'torch.nn.modules.normalization.LayerNorm'>": 0,}},])
-def layer_args(default_run_args, request):
+def layer_args_types_count(default_run_args, request):
     args = default_run_args
     layer_dict = request.param
-    exp_layer_types = layer_dict["exp_layer_types"]
     exp_layer_types_count = layer_dict["exp_layer_types_count"]
-    del layer_dict["exp_layer_types"]
     del layer_dict["exp_layer_types_count"]
     args.update(**layer_dict)
-    yield args, exp_layer_types, exp_layer_types_count
+    yield args, exp_layer_types_count
 
 
-def test_small_models_quant_layer(caplog, layer_args):
+@pytest.mark.llm
+@requires_pt_ge('2.2')
+def test_small_models_quant_layer_types_count(caplog, layer_args_types_count):
     caplog.set_level(logging.INFO)
-    args, exp_layer_types, exp_layer_types_count = layer_args
+    args, exp_layer_types_count = layer_args_types_count
     if args.replace_rmsnorm:
         if torch_version < version.parse('2.4'):
             pytest.skip("Replacing RMSNorm requires torch 2.4+ or greater")
         if hasattr(args, 'rotation') and args.rotation == 'fx' and platform.system() == 'Windows':
             pytest.skip("Skipping dynamo + windows")
     float_ppl, quant_ppl, model = validate_args_and_run_main(args)
-    # Naming of modules in the GraphModule generated by FX changes across transformers versions, e.g.
-    # (4.45.0)"L__self___model_layers_2_self_attn_k_proj" ->
-    # (4.46.0) 'L__self___model_layers_slice_None__2__None___0_self_attn_q_proj'
-    # Therefore, this check is skipped when rotation="fx".
-    if args.rotation != "fx":
-        assert_layer_types(model, exp_layer_types)
     assert_layer_types_count(model, exp_layer_types_count)