diff --git a/src/llmcompressor/modifiers/awq/base.py b/src/llmcompressor/modifiers/awq/base.py
index 51e8cf8b9..7dc442854 100644
--- a/src/llmcompressor/modifiers/awq/base.py
+++ b/src/llmcompressor/modifiers/awq/base.py
@@ -1,5 +1,5 @@
 import inspect
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 from compressed_tensors.quantization import (
@@ -12,7 +12,7 @@
     update_offload_parameter,
 )
 from loguru import logger
-from pydantic import ConfigDict, PrivateAttr, model_validator
+from pydantic import ConfigDict, PrivateAttr, field_validator, model_validator
 from torch.nn import Module
 from tqdm import tqdm
 
@@ -27,6 +27,7 @@
 from llmcompressor.modifiers.quantization.quantization import QuantizationMixin
 from llmcompressor.modifiers.utils.hooks import HooksMixin
 from llmcompressor.pipelines.cache import IntermediatesCache
+from llmcompressor.sentinel import Sentinel
 from llmcompressor.utils.fsdp.helpers import get_fsdp_parent
 from llmcompressor.utils.helpers import calibration_forward_context
 from llmcompressor.utils.pytorch.module import get_layer_by_name, get_layers
@@ -96,8 +97,6 @@ class AWQModifier(Modifier, QuantizationMixin):
         - on_finalize
             - clear resolved mappings and captured activations
 
-    :param sequential_targets: list of module names to compress in
-        the same calibration pass
     :param mappings: list activation layers to smooth, and which layers to
         scale the output such that activations are smoothed.
         Each entry of the mapping list should be a list itself, in which the first
@@ -116,11 +115,7 @@ class AWQModifier(Modifier, QuantizationMixin):
         and weights to determine the scaling factor
     """
 
-    # Allow arbitrary types because AWQMapping has fields of type torch.nn.Module
-    model_config: ConfigDict = ConfigDict(arbitrary_types_allowed=True)
-
     # User-provided vars (in addition to QuantizationMixin args)
-    sequential_targets: Union[str, List[str], None] = None
     mappings: Optional[List[AWQMapping]] = None
     offload_device: Optional[torch.device] = None
     duo_scaling: bool = True
@@ -141,6 +136,20 @@ class AWQModifier(Modifier, QuantizationMixin):
         default_factory=dict
     )
 
+    # deprecated
+    sequential_targets: Union[Sentinel, Any] = Sentinel("deprecated")
+
+    # Allow arbitrary types because AWQMapping has fields of type torch.nn.Module
+    model_config: ConfigDict = ConfigDict(arbitrary_types_allowed=True)
+
+    @field_validator("sequential_targets", mode="before")
+    def validate_sequential_targets(cls, value: bool) -> bool:
+        if value is not Sentinel("deprecated"):
+            raise ValueError(
+                "Setting `sequential_targets` via modifiers is no longer supported, "
+                "Please use `oneshot(sequential_targets=...)`"
+            )
+
     @model_validator(mode="after")
     def validate_model_after(model: "AWQModifier") -> "AWQModifier":
         """
diff --git a/src/llmcompressor/modifiers/obcq/base.py b/src/llmcompressor/modifiers/obcq/base.py
index 0dc69ae03..9595ba9c5 100644
--- a/src/llmcompressor/modifiers/obcq/base.py
+++ b/src/llmcompressor/modifiers/obcq/base.py
@@ -45,6 +45,10 @@ class SparseGPTModifier(SparsityModifierBase):
         - on_finalize
             - remove_hooks()
 
+    :param targets: list of module names to quantize if a scheme is provided. Defaults
+        to Linear layers
+    :param ignore: optional list of module class names or submodule names to not
+        quantize even if they match a target. Defaults to empty list.
     :param sparsity: Sparsity to compress model to
     :param sparsity_profile: Can be set to 'owl' to use Outlier Weighed
         Layerwise Sparsity (OWL), more information can be found
@@ -62,12 +66,6 @@ class SparseGPTModifier(SparsityModifierBase):
         previously pruned model, defaults to False.
     :param offload_hessians: Set to True for decreased memory usage but increased
         runtime.
-    :param sequential_targets: list of layer names to compress during OBCQ, or '__ALL__'
-        to compress every layer in the model. Alias for `targets`
-    :param targets: list of layer names to compress during OBCQ, or '__ALL__'
-        to compress every layer in the model. Alias for `sequential_targets`
-    :param ignore: optional list of module class names or submodule names to not
-        quantize even if they match a target. Defaults to empty list.
     """
 
     # modifier arguments
diff --git a/src/llmcompressor/modifiers/obcq/sgpt_base.py b/src/llmcompressor/modifiers/obcq/sgpt_base.py
index ce41273f3..b36a16d1b 100644
--- a/src/llmcompressor/modifiers/obcq/sgpt_base.py
+++ b/src/llmcompressor/modifiers/obcq/sgpt_base.py
@@ -8,13 +8,14 @@
 import torch
 from loguru import logger
 from pydantic import Field, PrivateAttr, field_validator, model_validator
+from transformers import PreTrainedModel
 
 from llmcompressor.core import Event, EventType, State
 from llmcompressor.modifiers.modifier import Modifier
 from llmcompressor.modifiers.utils.hooks import HooksMixin
+from llmcompressor.sentinel import Sentinel
 from llmcompressor.utils.pytorch.module import (
     get_layers,
-    get_no_split_params,
     get_prunable_layers,
     match_targets,
 )
@@ -27,18 +28,14 @@ class SparsityModifierBase(Modifier):
     """
 
     # modifier arguments
+    targets: Union[str, List[str]] = ["Linear"]
+    ignore: List[str] = Field(default_factory=list)
     sparsity: Optional[Union[float, List[float]]]
     sparsity_profile: Optional[str] = None
     mask_structure: str = "0:0"
     owl_m: Optional[int] = None
     owl_lmbda: Optional[float] = None
 
-    # data pipeline arguments
-    sequential_update: Optional[bool] = False  # deprecated
-    sequential_targets: Union[str, List[str], None] = None
-    targets: Union[str, List[str]] = ["Linear"]
-    ignore: List[str] = Field(default_factory=list)
-
     # private variables
     _prune_n: Optional[int] = PrivateAttr(default=None)
     _prune_m: Optional[int] = PrivateAttr(default=None)
@@ -46,16 +43,26 @@ class SparsityModifierBase(Modifier):
     _target_layers: Dict[str, torch.nn.Module] = PrivateAttr(default_factory=dict)
     _module_sparsities: Dict[torch.nn.Module, str] = PrivateAttr(default_factory=dict)
 
+    # deprecated
+    sequential_update: Union[Sentinel, Any] = Sentinel("deprecated")
+    sequential_targets: Union[Sentinel, Any] = Sentinel("deprecated")
+
     @field_validator("sequential_update", mode="before")
     def validate_sequential_update(cls, value: bool) -> bool:
-        if not value:
+        if value is not Sentinel("deprecated"):
             warnings.warn(
                 "`sequential_update=False` is no longer supported, setting "
                 "sequential_update=True",
                 DeprecationWarning,
             )
 
-        return True
+    @field_validator("sequential_targets", mode="before")
+    def validate_sequential_targets(cls, value: bool) -> bool:
+        if value is not Sentinel("deprecated"):
+            raise ValueError(
+                "Setting `sequential_targets` via modifiers is no longer supported, "
+                "Please use `oneshot(sequential_targets=...)`"
+            )
 
     @field_validator("sparsity_profile", mode="before")
     def validate_sparsity_profile(cls, value: Optional[str]) -> bool:
@@ -109,12 +116,12 @@ def on_initialize(self, state: "State", **kwargs) -> bool:
 
         :param state: session state storing input model and calibration data
         """
-        model: torch.nn.Module = state.model
+        model: PreTrainedModel = state.model
         dataloader: torch.utils.data.DataLoader = state.data.calib
 
         # infer module and sequential targets
-        self.sequential_targets = self._infer_sequential_targets(model)
-        layers = get_layers(self.sequential_targets, model)
+        sequential_targets = model._get_no_split_modules("auto")
+        layers = get_layers(sequential_targets, model)
         self._target_layers = get_layers(
             self.targets, model
         )  # layers containing targets
@@ -191,15 +198,6 @@ def on_end(self, state: State, event: Event, **kwargs):
         self.ended_ = True
         self.remove_hooks()
 
-    def _infer_sequential_targets(
-        self, model: torch.nn.Module
-    ) -> Union[str, List[str]]:
-        if self.sequential_targets is None:
-            return get_no_split_params(model)
-        if isinstance(self.sequential_targets, str):
-            return [self.sequential_targets]
-        return self.sequential_targets
-
     def _infer_owl_layer_sparsity(
         self,
         model: torch.nn.Module,
diff --git a/src/llmcompressor/modifiers/pruning/wanda/base.py b/src/llmcompressor/modifiers/pruning/wanda/base.py
index de562f6d7..5d6673411 100644
--- a/src/llmcompressor/modifiers/pruning/wanda/base.py
+++ b/src/llmcompressor/modifiers/pruning/wanda/base.py
@@ -36,14 +36,15 @@ class WandaPruningModifier(SparsityModifierBase):
     Lifecycle:
         - on_initialize
             - register_hook(module, calibrate_module, "forward")
-            - run_sequential / run_layer_sequential / run_basic
-                - make_empty_row_scalars
-                - accumulate_row_scalars
         - on_sequential_batch_end
             - sparsify_weight
         - on_finalize
             - remove_hooks()
 
+    :param targets: list of module names to quantize if a scheme is provided. Defaults
+        to Linear layers
+    :param ignore: optional list of module class names or submodule names to not
+        quantize even if they match a target. Defaults to empty list.
     :param sparsity: Sparsity to compress model to
     :param sparsity_profile: Can be set to 'owl' to use Outlier Weighed
         Layerwise Sparsity (OWL), more information can be found
@@ -53,12 +54,6 @@ class WandaPruningModifier(SparsityModifierBase):
         shape. Defaults to 0:0 which represents an unstructured mask.
     :param owl_m: Number of outliers to use for OWL
     :param owl_lmbda: Lambda value to use for OWL
-    :param sequential_targets: list of layer names to compress during OBCQ, or '__ALL__'
-        to compress every layer in the model. Alias for `targets`
-    :param targets: list of layer names to compress during OBCQ, or '__ALL__'
-        to compress every layer in the model. Alias for `sequential_targets`
-    :param ignore: optional list of module class names or submodule names to not
-        quantize even if they match a target. Defaults to empty list.
     """
 
     # private variables
diff --git a/src/llmcompressor/modifiers/quantization/gptq/base.py b/src/llmcompressor/modifiers/quantization/gptq/base.py
index 7ae61f3e2..147f9b514 100644
--- a/src/llmcompressor/modifiers/quantization/gptq/base.py
+++ b/src/llmcompressor/modifiers/quantization/gptq/base.py
@@ -1,6 +1,6 @@
 import contextlib
 import warnings
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 from compressed_tensors.quantization import (
@@ -61,7 +61,7 @@ class GPTQModifier(Modifier, QuantizationMixin):
 
     Lifecycle:
         - on_initialize
-            - apply config to model
+            - apply quantization config to model
         - on_start
             - add activation calibration hooks
             - add gptq weight calibration hooks
@@ -71,8 +71,6 @@ class GPTQModifier(Modifier, QuantizationMixin):
             - remove_hooks()
             - model.apply(freeze_module_quantization)
 
-    :param sequential_targets: list of layer names to compress during GPTQ, or
-        '__ALL__' to compress every layer in the model
     :param block_size: Used to determine number of columns to compress in one pass
     :param dampening_frac: Amount of dampening to apply to H, as a fraction of the
         diagonal norm
@@ -83,7 +81,7 @@ class GPTQModifier(Modifier, QuantizationMixin):
 
     :param config_groups: dictionary specifying quantization schemes to apply to target
         modules. Modules not matching a scheme target will NOT be quantized.
-    :param targets: list of layer names to quantize if a scheme is provided. Defaults
+    :param targets: list of module names to quantize if a scheme is provided. Defaults
         to Linear layers
     :param ignore: optional list of module class names or submodule names to not
         quantize even if they match a target in config_groups. Defaults to empty list.
@@ -106,8 +104,6 @@ class GPTQModifier(Modifier, QuantizationMixin):
     """
 
     # gptq modifier arguments
-    sequential_update: bool = True  # DEPRECATED
-    sequential_targets: Union[str, List[str], None] = None
     block_size: int = 128
     dampening_frac: Optional[float] = 0.01
     actorder: Optional[Union[ActivationOrdering, Sentinel]] = None
@@ -118,16 +114,26 @@ class GPTQModifier(Modifier, QuantizationMixin):
     _hessians: Dict[torch.nn.Module, torch.Tensor] = PrivateAttr(default_factory=dict)
     _num_samples: Dict[torch.nn.Module, int] = PrivateAttr(default_factory=dict)
 
+    # deprecated
+    sequential_update: Union[Sentinel, Any] = Sentinel("deprecated")
+    sequential_targets: Union[Sentinel, Any] = Sentinel("deprecated")
+
     @field_validator("sequential_update", mode="before")
     def validate_sequential_update(cls, value: bool) -> bool:
-        if not value:
+        if value is not Sentinel("deprecated"):
             warnings.warn(
                 "`sequential_update=False` is no longer supported, setting "
                 "sequential_update=True",
                 DeprecationWarning,
             )
 
-        return True
+    @field_validator("sequential_targets", mode="before")
+    def validate_sequential_targets(cls, value: bool) -> bool:
+        if value is not Sentinel("deprecated"):
+            raise ValueError(
+                "Setting `sequential_targets` via modifiers is no longer supported, "
+                "Please use `oneshot(sequential_targets=...)`"
+            )
 
     def resolve_quantization_config(self) -> QuantizationConfig:
         config = super().resolve_quantization_config()
diff --git a/src/llmcompressor/pipelines/layer_sequential/helpers.py b/src/llmcompressor/pipelines/layer_sequential/helpers.py
index c8b9fcbd3..c7e177411 100644
--- a/src/llmcompressor/pipelines/layer_sequential/helpers.py
+++ b/src/llmcompressor/pipelines/layer_sequential/helpers.py
@@ -69,7 +69,7 @@ def capture_first_layer_intermediates(
         desc = "Preparing intermediates cache"
         for batch_index, batch in enumerate(tqdm.tqdm(dataloader, desc=desc)):
             batch = apply_pad_mask_to_batch(batch) if mask_padding else batch
-            batch = tensors_to_device(batch, model_device)
+            batch = tensors_to_device(batch, torch.device("cpu"))
 
             try:
                 model(**batch)
diff --git a/src/llmcompressor/pipelines/layer_sequential/pipeline.py b/src/llmcompressor/pipelines/layer_sequential/pipeline.py
index e5a608708..650d5a66a 100644
--- a/src/llmcompressor/pipelines/layer_sequential/pipeline.py
+++ b/src/llmcompressor/pipelines/layer_sequential/pipeline.py
@@ -5,7 +5,7 @@
 from compressed_tensors.utils import disable_offloading, get_execution_device
 from torch.utils.data.dataloader import DataLoader
 
-from llmcompressor.core import LifecycleCallbacks, active_session
+from llmcompressor.core import LifecycleCallbacks
 from llmcompressor.modifiers.utils.hooks import HooksMixin
 from llmcompressor.pipelines.cache import IntermediatesCache
 from llmcompressor.pipelines.layer_sequential.helpers import (
@@ -17,7 +17,7 @@
 from llmcompressor.pipelines.registry import CalibrationPipeline
 from llmcompressor.pipelines.sequential.helpers import (
     dispatch_for_sequential,
-    get_sequential_targets,
+    infer_sequential_targets,
 )
 from llmcompressor.utils.helpers import DisableQuantization, calibration_forward_context
 
@@ -56,15 +56,17 @@ def __call__(
         :param dataloader: loads data for calibration
         :param dataset_args: dataset arguments relevant to pipelines
         """
-        session = active_session()
+        # prepare model for sequential onloading
+        dispatch_for_sequential(model)
 
         # prepare model for sequential onloading
         dispatch_for_sequential(model)
         model_device = get_execution_device(model)
 
         # find layers
-        modifiers = session.get_modifiers()
-        sequential_targets = get_sequential_targets(modifiers, model, dataset_args)
+        sequential_targets = infer_sequential_targets(
+            model, dataset_args.sequential_targets
+        )
         layers = match_modules(model, sequential_targets)
 
         LifecycleCallbacks.calibration_epoch_start()
diff --git a/src/llmcompressor/pipelines/sequential/helpers.py b/src/llmcompressor/pipelines/sequential/helpers.py
index 4f562818a..b374ceb24 100644
--- a/src/llmcompressor/pipelines/sequential/helpers.py
+++ b/src/llmcompressor/pipelines/sequential/helpers.py
@@ -2,7 +2,7 @@
 import inspect
 from collections import deque
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Optional, Set
 
 import torch
 from compressed_tensors.quantization import find_name_or_class_matches
@@ -20,20 +20,15 @@
 from transformers.configuration_utils import PretrainedConfig
 from transformers.utils.fx import HFTracer
 
-from llmcompressor.modifiers import Modifier
 from llmcompressor.modifiers.utils.hooks import HooksMixin
 from llmcompressor.utils.helpers import calibration_forward_context, patch_attr
-from llmcompressor.utils.pytorch.module import get_no_split_params
 
 from .ast_helpers import autowrap_forwards
 
-if TYPE_CHECKING:
-    from llmcompressor.args.dataset_arguments import DatasetArguments
-
 __all__ = [
     "trace_subgraphs",
     "Subgraph",
-    "get_sequential_targets",
+    "infer_sequential_targets",
     "dispatch_for_sequential",
 ]
 
@@ -428,59 +423,21 @@ def match_modules(model: Module, target_names: List[str]) -> Set[Module]:
     )
 
 
-def get_sequential_targets(
-    modifiers: List[Modifier], model: PreTrainedModel, args: "DatasetArguments"
+def infer_sequential_targets(
+    model: PreTrainedModel, sequential_targets: Optional[List[str]]
 ) -> List[str]:
     """
-    Infer sequential targets from modifiers list and dataset args
+    Infer sequential targets dataset args
 
     :param model: model being calibrated
-    :param modifiers: list of modifiers being applied during calibration
-    :param dataset_args: dataset arguments passed by user
+    :param sequential_targets: optional targets passed by user
     :return: list of sequential targets
     """
-    modifier_targets = [
-        (modifier, modifier.sequential_targets)
-        for modifier in modifiers
-        if getattr(modifier, "sequential_targets", None) is not None
-    ]
-
-    # deprecation warning
-    if len(modifier_targets) >= 1:
-        logger.warning(
-            "Passing sequential targets through modifiers is deprecated, "
-            "please use `oneshot(sequential_targets=...)`"
-        )
+    if not sequential_targets:
+        sequential_targets = model._get_no_split_modules("auto")
 
-    # cannot infer from multiple modifiers
-    if len(modifier_targets) >= 2:
-        types = [type(modifier) for modifier, _ in modifier_targets]
-        raise ValueError(
-            "Cannot infer sequential targets from multiple sequential modifiers "
-            f"({types})"
-        )
-
-    # resolve single modifier
-    if len(modifier_targets) == 1:
-        if args.sequential_targets is not None:
-            raise ValueError(
-                f"Got sequential targets from both {type(modifier_targets[0][0])} "
-                "and dataset arguments `sequential_targets`"
-            )
-
-        sequential_targets = modifier_targets[0][1]
-
-    # if no modifiers, use data args
-    else:
-        sequential_targets = args.sequential_targets  # may be `None`
-
-    # validate and infer
-    if sequential_targets is None:
-        return get_no_split_params(model)
-    elif isinstance(sequential_targets, str):
-        return [sequential_targets]
-    else:
-        return sequential_targets
+    if len(sequential_targets) <= 0:
+        return ["Linear"]
 
 
 def add_line_numbers(text: str) -> str:
diff --git a/src/llmcompressor/pipelines/sequential/pipeline.py b/src/llmcompressor/pipelines/sequential/pipeline.py
index 9a2b8f3c9..3495941a3 100644
--- a/src/llmcompressor/pipelines/sequential/pipeline.py
+++ b/src/llmcompressor/pipelines/sequential/pipeline.py
@@ -5,13 +5,13 @@
 from torch.utils.data.dataloader import DataLoader
 from tqdm import tqdm
 
-from llmcompressor.core import LifecycleCallbacks, active_session
+from llmcompressor.core import LifecycleCallbacks
 from llmcompressor.modifiers.utils.hooks import HooksMixin
 from llmcompressor.pipelines.cache import IntermediatesCache
 from llmcompressor.pipelines.registry import CalibrationPipeline
 from llmcompressor.pipelines.sequential.helpers import (
     dispatch_for_sequential,
-    get_sequential_targets,
+    infer_sequential_targets,
     trace_subgraphs,
 )
 from llmcompressor.utils.helpers import DisableQuantization, calibration_forward_context
@@ -50,15 +50,17 @@ def __call__(
         :param dataloader: loads data for calibration
         :param dataset_args: dataset arguments relevant to pipelines
         """
-        session = active_session()
+        # prepare model for sequential onloading
+        dispatch_for_sequential(model)
 
         # prepare model for sequential onloading
         dispatch_for_sequential(model)
         model_device = get_execution_device(model)
 
         # prepare to trace subgraphs
-        modifiers = session.get_modifiers()
-        sequential_targets = get_sequential_targets(modifiers, model, dataset_args)
+        sequential_targets = infer_sequential_targets(
+            model, dataset_args.sequential_targets
+        )
         ignore = dataset_args.tracing_ignore
 
         # trace subgraphs
diff --git a/src/llmcompressor/transformers/tracing/debug.py b/src/llmcompressor/transformers/tracing/debug.py
index bb70bf712..bb1970c30 100644
--- a/src/llmcompressor/transformers/tracing/debug.py
+++ b/src/llmcompressor/transformers/tracing/debug.py
@@ -7,8 +7,9 @@
 import transformers
 from transformers import AutoProcessor, PreTrainedModel
 
-from llmcompressor.utils.pytorch.module import get_no_split_params
-from llmcompressor.pipelines.sequential.helpers import trace_subgraphs, Subgraph
+from llmcompressor.pipelines.sequential.helpers import (
+    infer_sequential_targets, trace_subgraphs, Subgraph
+)
 from llmcompressor.transformers import TextGenerationDataset
 from llmcompressor.args import DatasetArguments
 
@@ -86,10 +87,9 @@ def trace(
     print("Loaded sample data")
 
     # infer sequential targets
-    if sequential_targets is None:
-        sequential_targets = get_no_split_params(model)
-    if isinstance(sequential_targets, str):
-        sequential_targets = [sequential_targets]
+    sequential_targets = infer_sequential_targets(
+        model, sequential_targets
+    )
 
     # Attempt trace
     print(
diff --git a/src/llmcompressor/utils/pytorch/module.py b/src/llmcompressor/utils/pytorch/module.py
index 835493fa3..dc19185a5 100644
--- a/src/llmcompressor/utils/pytorch/module.py
+++ b/src/llmcompressor/utils/pytorch/module.py
@@ -12,7 +12,6 @@
 from packaging import version
 from torch.nn import Linear, Module, Parameter
 from torch.nn.modules.conv import _ConvNd
-from transformers import PreTrainedModel
 
 from llmcompressor.core import ModelParameterizedLayer
 from llmcompressor.utils.fsdp.context import (
@@ -60,7 +59,6 @@
     "qat_active",
     "get_layers_params",
     "get_matching_layer",
-    "get_no_split_params",
     "get_layer_by_name",
 ]
 
@@ -316,25 +314,6 @@ def get_matching_layer(
     return match
 
 
-def get_no_split_params(model: PreTrainedModel) -> Union[str, List[str]]:
-    """
-    Get list of module classes that shouldn't be split when sharding. For
-    Hugging Face Transformer models, this is the decoder layer type. For other
-    types of models, this just returns all module names.
-
-    :return: list of class names that shouldn't be split
-    """
-    # importing here to avoid circular import
-    from llmcompressor.utils.fsdp.helpers import maybe_get_wrapped
-
-    model = maybe_get_wrapped(model)
-    no_split_modules = model._get_no_split_modules("auto")
-    if len(no_split_modules) <= 0:
-        return ALL_TARGET
-
-    return no_split_modules
-
-
 # https://discuss.pytorch.org/t/how-to-access-to-a-layer-by-module-name/83797/8
 def get_layer_by_name(layer_name: str, module: Module) -> Module:
     """
diff --git a/tests/llmcompressor/transformers/obcq/test_obcq_infer_targets.py b/tests/llmcompressor/transformers/obcq/test_obcq_infer_targets.py
deleted file mode 100644
index abe74da19..000000000
--- a/tests/llmcompressor/transformers/obcq/test_obcq_infer_targets.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import pytest
-from accelerate import init_empty_weights
-from transformers import AutoModelForCausalLM
-
-from llmcompressor.modifiers.obcq import SparseGPTModifier
-
-
-@pytest.mark.integration
-def test_infer_targets():
-    modifier = SparseGPTModifier(sparsity=0.0)
-    with init_empty_weights():
-        model = AutoModelForCausalLM.from_pretrained("nm-testing/llama2.c-stories15M")
-
-    inferred = modifier._infer_sequential_targets(model)
-    assert inferred == ["LlamaDecoderLayer"]
diff --git a/tests/llmcompressor/transformers/obcq/test_obcq_owl.py b/tests/llmcompressor/transformers/obcq/test_obcq_owl.py
index a1de2533d..dea55d927 100644
--- a/tests/llmcompressor/transformers/obcq/test_obcq_owl.py
+++ b/tests/llmcompressor/transformers/obcq/test_obcq_owl.py
@@ -28,7 +28,7 @@ def test_infer_owl_layer_sparsity():
         )
         dataloader = format_calibration_data(dataset)
 
-        sequential_targets = modifier._infer_sequential_targets(model)
+        sequential_targets = model._get_no_split_modules("auto")
         layers = get_layers(sequential_targets, model)
         sparsities = modifier._infer_owl_layer_sparsity(model, layers, dataloader)
         assert sparsities.keys() == layers.keys()
diff --git a/tests/llmcompressor/transformers/tracing/test_models.py b/tests/llmcompressor/transformers/tracing/test_models.py
index fba0ffe56..5603994a9 100644
--- a/tests/llmcompressor/transformers/tracing/test_models.py
+++ b/tests/llmcompressor/transformers/tracing/test_models.py
@@ -14,9 +14,11 @@
     WhisperForConditionalGeneration,
 )
 
-from llmcompressor.pipelines.sequential.helpers import match_modules
+from llmcompressor.pipelines.sequential.helpers import (
+    infer_sequential_targets,
+    match_modules,
+)
 from llmcompressor.transformers.tracing.debug import trace
-from llmcompressor.utils.pytorch.module import get_no_split_params
 
 
 @pytest.mark.skipif(
@@ -143,11 +145,7 @@ def test_model_trace(model_id, model_class, targets, modality, backends):
 
 
 def get_target_modules(model, sequential_targets):
-    if sequential_targets is None:
-        sequential_targets = get_no_split_params(model)
-    if isinstance(sequential_targets, str):
-        sequential_targets = [sequential_targets]
-
+    sequential_targets = infer_sequential_targets(model, sequential_targets)
     return match_modules(model, sequential_targets)