diff --git a/compress_model.py b/compress_model.py
new file mode 100644
index 000000000..fa67bead0
--- /dev/null
+++ b/compress_model.py
@@ -0,0 +1,60 @@
+# python3 compress_model.py --model_id meta-llama/Llama-3.2-1B-Instruct --transform_type random-hadamard
+import argparse
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.transform import SpinQuantModifier
+from llmcompressor.utils import dispatch_for_generation
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_id", type=str, help="Model stub to compress")
+    parser.add_argument("--transform_type", type=str, default=None, help="Type of transform used in SpinQuantModifier")
+    parser.add_argument("--scheme", type=str, default=None, help="Quantization scheme (e.g. W4A16)")
+    return parser.parse_args()
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    # Select model and load it.
+    MODEL_ID = args.model_id
+    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+    # Select number of samples. 512 samples is a good place to start.
+    # Increasing the number of samples can improve accuracy.
+    NUM_CALIBRATION_SAMPLES = 512
+    MAX_SEQUENCE_LENGTH = 2048
+
+    # Configure the quantization algorithm to run.
+    recipe = []
+    if args.transform_type:
+        recipe.append(SpinQuantModifier(rotations=["R1", "R2"], transform_type=args.transform_type))
+
+    if args.scheme:
+        recipe.append(QuantizationModifier(targets="Linear", scheme=args.scheme, ignore=["lm_head"]))
+
+    # Apply algorithms.
+    oneshot(
+        model=model,
+        recipe=recipe,
+        dataset="ultrachat_200k",
+        splits={"calibration": f"train_sft[:{NUM_CALIBRATION_SAMPLES}]"},
+        max_seq_length=MAX_SEQUENCE_LENGTH,
+        num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    )
+
+    # Confirm generations of the quantized model look sane.
+    print("\n\n")
+    print("========== SAMPLE GENERATION ==============")
+    dispatch_for_generation(model)
+    input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+    output = model.generate(input_ids, max_new_tokens=100)
+    print(tokenizer.decode(output[0]))
+    print("==========================================\n\n")
+
+    # Save to disk compressed.
+    SAVE_DIR = MODEL_ID.split("/")[1] + f"-{args.transform_type}-{args.scheme}"
+    model.save_pretrained(SAVE_DIR, save_compressed=True)
+    tokenizer.save_pretrained(SAVE_DIR)
diff --git a/examples/transform/spinquant_example.py b/examples/transform/spinquant_example.py
new file mode 100644
index 000000000..876db7138
--- /dev/null
+++ b/examples/transform/spinquant_example.py
@@ -0,0 +1,86 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.transform import SpinQuantModifier
+from llmcompressor.utils import dispatch_for_generation
+
+# Select model and load it.
+MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Select calibration dataset.
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+# Configure the quantization algorithm to run.
+#   * apply spinquant transforms to model in order to make quantization easier
+#   * quantize the weights to 4 bit with GPTQ with a group size 128
+recipe = [
+    SpinQuantModifier(rotations=["R1", "R2"], transform_type="hadamard"),
+    QuantizationModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"]),
+]
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    recipe=recipe,
+    dataset=ds,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+)
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+dispatch_for_generation(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=100)
+print(tokenizer.decode(output[0]))
+print("==========================================\n\n")
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-transformed-w4a16"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
diff --git a/src/llmcompressor/modeling/__init__.py b/src/llmcompressor/modeling/__init__.py
index e2c22ed1f..76b6b0391 100644
--- a/src/llmcompressor/modeling/__init__.py
+++ b/src/llmcompressor/modeling/__init__.py
@@ -1,3 +1,4 @@
 # flake8: noqa
 
+from .fuse import *
 from .prepare import *
diff --git a/src/llmcompressor/modeling/fuse.py b/src/llmcompressor/modeling/fuse.py
index a8168a31f..e59be596c 100644
--- a/src/llmcompressor/modeling/fuse.py
+++ b/src/llmcompressor/modeling/fuse.py
@@ -6,55 +6,58 @@
     get_execution_device,
     update_offload_parameter,
 )
+from transformers.models.llama.modeling_llama import LlamaRMSNorm
 
-__all__ = ["center_embeddings", "fuse_norm_linears"]
+__all__ = ["normalize_embedding", "fuse_norm_linears"]
 
 
 PRECISION = torch.float64
 
 
-def center_embeddings(embedding: torch.nn.Module):
+def normalize_embedding(embedding: torch.nn.Module):
     """
-    Shift each embedding to have a mean of zero
+    Normalize each embedding to have a mean of zero
 
     :param embedding: embedding module containing embeddings to center
     """
-    if not hasattr(embedding, "weight"):
-        raise ValueError(f"Cannot fuse norm of type {type(embedding)}")
+    if isinstance(embedding, (torch.nn.Embedding)):
+        with align_module_device(embedding):
+            weight_dtype = embedding.weight.dtype
+            weight = embedding.weight.to(PRECISION)
+            new_weight = weight - weight.mean(dim=-1, keepdim=True)
+            new_weight = new_weight.to(weight_dtype)
 
-    with align_module_device(embedding):
-        weight_dtype = embedding.weight.dtype
-        weight = embedding.weight.to(PRECISION)
-        new_weight = weight - weight.mean(dim=-1, keepdim=True)
-        new_weight = new_weight.to(weight_dtype)
+        update_offload_parameter(embedding, "weight", new_weight)
 
-    update_offload_parameter(embedding, "weight", new_weight)
+    else:
+        raise ValueError(f"Cannot normalize embedding of type {type(embedding)}")
 
 
 def fuse_norm_linears(norm: torch.nn.Module, linears: Iterable[torch.nn.Linear]):
     """
-    Fuse the scaling operation of norm layer into subsequent linear layers.
-    This useful for ensuring transform invariance between norm and linear layers.
+    Fuse a norm layer into subsequent linear layers. This useful for ensuring transform
+    invariance between norm and linear layers.
 
-    Note that unitary transforms (rotation) commute with normalization, but not scaling
+    Note that a model cannot be properly trained after its norms have been fused
 
     :param norm: norm layer whose weight will be fused into subsequent linears
     :param linears: linear layers which directly follow the norm layer
     """
-    if not hasattr(norm, "weight"):
+    if isinstance(norm, (torch.nn.RMSNorm, LlamaRMSNorm, torch.nn.LayerNorm)):
+        for linear in linears:
+            # NOTE: spinquant does this op in float64
+            exec_device = get_execution_device(norm)
+            with align_module_device(norm, exec_device), align_module_device(
+                linear, exec_device
+            ):
+                weight_dtype = linear.weight.dtype
+                new_weight = linear.weight.to(PRECISION) * norm.weight.to(PRECISION)
+                new_weight = new_weight.to(weight_dtype)
+
+            update_offload_parameter(linear, "weight", new_weight)
+
+        new_norm_weight = torch.ones_like(norm.weight, device="cpu")
+        update_offload_parameter(norm, "weight", new_norm_weight)
+
+    else:
         raise ValueError(f"Cannot fuse norm of type {type(norm)}")
-
-    for linear in linears:
-        # NOTE: spinquant does this op in float64
-        exec_device = get_execution_device(norm)
-        with align_module_device(norm, exec_device), align_module_device(
-            linear, exec_device
-        ):
-            weight_dtype = linear.weight.dtype
-            new_weight = linear.weight.to(PRECISION) * norm.weight.to(PRECISION)
-            new_weight = new_weight.to(weight_dtype)
-
-        update_offload_parameter(linear, "weight", new_weight)
-
-    new_norm_weight = torch.ones_like(norm.weight, device="cpu")
-    update_offload_parameter(norm, "weight", new_norm_weight)
diff --git a/src/llmcompressor/modifiers/transform/__init__.py b/src/llmcompressor/modifiers/transform/__init__.py
new file mode 100644
index 000000000..9956d0340
--- /dev/null
+++ b/src/llmcompressor/modifiers/transform/__init__.py
@@ -0,0 +1,3 @@
+# flake8: noqa
+
+from .spinquant import SpinQuantModifier
diff --git a/src/llmcompressor/modifiers/transform/spinquant/__init__.py b/src/llmcompressor/modifiers/transform/spinquant/__init__.py
new file mode 100644
index 000000000..8bdc93d14
--- /dev/null
+++ b/src/llmcompressor/modifiers/transform/spinquant/__init__.py
@@ -0,0 +1,3 @@
+# flake8: noqa
+
+from .base import *
diff --git a/src/llmcompressor/modifiers/transform/spinquant/base.py b/src/llmcompressor/modifiers/transform/spinquant/base.py
new file mode 100644
index 000000000..5978b93ea
--- /dev/null
+++ b/src/llmcompressor/modifiers/transform/spinquant/base.py
@@ -0,0 +1,221 @@
+from enum import Enum
+from typing import Iterable, List, Literal, Optional
+
+from compressed_tensors import match_modules_set, match_named_modules
+from compressed_tensors.transform import (
+    TransformArgs,
+    TransformConfig,
+    TransformScheme,
+    apply_transform_config,
+)
+from pydantic import Field, ValidationInfo, field_validator
+from transformers import PreTrainedModel
+
+from llmcompressor.core import Event, EventType, State
+from llmcompressor.modeling import fuse_norm_linears, normalize_embedding
+from llmcompressor.modifiers import Modifier
+
+from .mappings import SpinQuantMapping, infer_mapping_from_model
+from .norm_mappings import NormMapping, infer_norm_mapping_from_model
+
+
+class SpinquantRotation(str, Enum):
+    R1 = "R1"
+    R2 = "R2"
+    R3 = "R3"
+    R4 = "R4"
+
+
+class SpinQuantModifier(Modifier, use_enum_values=True):
+    """
+    Implements the transforms according to
+    [SpinQuant: LLM quantization with learned rotations](https://arxiv.org/abs/2405.16406)  # noqa: E501
+
+    Transforms (rotations) are extra layers added to a model which reduce the accuracy
+    loss induced by quantization. This is achived through "rotating" weights and
+    activations into a space with a smaller dynamic range of values, thus decreasing
+    the range of scales required for quantization.
+
+    The SpinQuant authors describe four different rotations which can be applied to a
+    model. R1 and R2 are "offline" rotations, meaning that they can be fused into
+    existing weights and therefore do not induce runtime cost. R3 and R4 are "online"
+    rotations, meaning that they require additional computation at runtime.
+
+    :param rotations: A list containing the names of rotations to apply to the model.
+        Possible rotations include R1, R2, R3, and R4
+    :param transform_type: The type of transform to apply to the model.
+        `"hadamard"` has the least performance cost but only supports sizes which are
+        powers of power of two.
+        `"random-matrix"` has more performance cost, but supports a much larger set of
+            sizes.
+        `"random-matrix"` has the greatest performance cost, but supports any size
+    :param randomize: if True, create distinct transforms for each application
+    :param learnable: if True, attach gradients to transform weights for training
+    :param mappings: Specifies layers within a model to target for transforms.
+        A mapping will be inferred if None is provided
+    :param norm_mappings: Specifies layers within a model to target for norm fusing.
+        A mapping will be inferred if None is provided
+    :param transform_config: Optional transform config for overriding provided arguments
+    """
+
+    rotations: List[SpinquantRotation] = Field(
+        default_factory=lambda: ["R1", "R2"], exclude=True
+    )
+    transform_type: Literal["hadamard", "random-hadamard", "random-matrix"] = Field(
+        default="hadamard", exclude=True
+    )
+    randomize: bool = Field(default=False, exclude=True)
+    learnable: bool = Field(default=False, exclude=True)
+
+    # norm mappings separate from spinquant mappings to allow users to
+    # override spinquant mappings with transform_config without overriding norms
+    mappings: Optional[SpinQuantMapping] = Field(
+        default=None,
+        repr=False,
+        exclude=True,
+    )
+    norm_mappings: Optional[List[NormMapping]] = Field(
+        default=None,
+        repr=False,
+        exclude=True,
+    )
+
+    # optional override for more fine-grained control
+    # also included in recipe serialization
+    transform_config: Optional[TransformConfig] = Field(default=None, repr=False)
+
+    @field_validator("randomize", "learnable", mode="before")
+    def validate_not_implemented(cls, value, info: ValidationInfo):
+        raise NotImplementedError(f"{info.field_name} is not supported right now")
+
+    @field_validator("rotations", mode="before")
+    def validate_rotations(cls, value):
+        if isinstance(value, Iterable):
+            return tuple(v.upper() for v in value)
+        return value
+
+    def on_initialize(self, state: State, **kwargs) -> bool:
+        if self.transform_config is not None:
+            return True
+
+        self.mappings = infer_mapping_from_model(state.model)
+        self.norm_mappings = infer_norm_mapping_from_model(state.model)
+
+        config_groups = {}
+        if SpinquantRotation.R1 in self.rotations:
+            config_groups["R1"] = self._create_r1_scheme()
+
+        if SpinquantRotation.R2 in self.rotations:
+            config_groups["R2"] = self._create_r2_scheme(state.model)
+
+        if SpinquantRotation.R3 in self.rotations:
+            config_groups["R3"] = self._create_r3_scheme()
+
+        if SpinquantRotation.R4 in self.rotations:
+            config_groups["R4"] = self._create_r4_scheme()
+
+        self.transform_config = TransformConfig(config_groups=config_groups)
+
+        return True
+
+    def on_start(self, state: State, event: Event, **kwargs):
+        self.started_ = True
+
+        # needs to happen after the model has been hooked to execute on the GPU
+        # otherwise we're applying weight transforms on CPU
+        self._prenormalize_embeddings(state.model)
+        self._fuse_norms(state.model)
+        apply_transform_config(state.model, self.transform_config)
+
+    def on_event(self, state: State, event: Event, **kwargs):
+        if event.type_ == EventType.CALIBRATION_EPOCH_START:
+            if not self.started_:
+                self.on_start(state, None)
+
+        elif event.type_ == EventType.SEQUENTIAL_EPOCH_END:
+            pass
+
+        elif event.type_ == EventType.CALIBRATION_EPOCH_END:
+            if not self.ended_:
+                self.on_end(state, None)
+
+    def on_end(self, state: State, event: Event, **kwargs):
+        self.ended_ = True
+
+    def on_finalize(self, state: State, **kwargs) -> bool:
+        if not self.ended_:
+            self.on_end(state, None)
+
+        return True
+
+    def _prenormalize_embeddings(self, model: PreTrainedModel):
+        for _, embedding in match_named_modules(
+            model, [self.mappings.embedding], warn_on_fail=True
+        ):
+            normalize_embedding(embedding)
+
+    def _fuse_norms(self, model: PreTrainedModel):
+        for mapping in self.norm_mappings:
+            for norm, *linears in match_modules_set(
+                model, (mapping.norm, *mapping.linears)
+            ):
+                fuse_norm_linears(norm, linears)
+
+    def _create_r1_scheme(self) -> TransformScheme:
+        return TransformScheme(
+            type=self.transform_type,
+            randomize=self.randomize,
+            requires_grad=self.learnable,
+            apply=[
+                TransformArgs(
+                    targets=[
+                        self.mappings.embedding,
+                        self.mappings.attn_o,
+                        *self.mappings.mlp_out,
+                    ],
+                    location="weight_output",
+                ),
+                TransformArgs(
+                    targets=[
+                        self.mappings.attn_q,
+                        self.mappings.attn_k,
+                        self.mappings.attn_v,
+                        *self.mappings.mlp_in,
+                        self.mappings.lm_head,
+                    ],
+                    location="weight_input",
+                    inverse=True,
+                ),
+            ],
+        )
+
+    def _create_r2_scheme(self, model: PreTrainedModel) -> TransformScheme:
+        config = model.config
+
+        if hasattr(config, "head_dim"):
+            head_dim = config.head_dim
+        elif hasattr(config, "hidden_size") and hasattr(config, "num_attention_heads"):
+            head_dim = config.hidden_size // config.num_attention_heads
+        else:
+            raise NotImplementedError()
+
+        return TransformScheme(
+            type=self.transform_type,
+            randomize=self.randomize,
+            requires_grad=self.learnable,
+            head_dim=head_dim,
+            apply=[
+                TransformArgs(targets=[self.mappings.attn_v], location="weight_output"),
+                TransformArgs(
+                    targets=[self.mappings.attn_o],
+                    location="weight_input",
+                    inverse=True,
+                ),
+            ],
+        )
+
+    def _create_r3_scheme(self) -> TransformScheme:
+        raise NotImplementedError()
+
+    def _create_r4_scheme(self) -> TransformScheme:
+        raise NotImplementedError()
diff --git a/src/llmcompressor/modifiers/transform/spinquant/mappings.py b/src/llmcompressor/modifiers/transform/spinquant/mappings.py
new file mode 100644
index 000000000..7dc327b78
--- /dev/null
+++ b/src/llmcompressor/modifiers/transform/spinquant/mappings.py
@@ -0,0 +1,57 @@
+from typing import Dict, List, Optional
+
+from loguru import logger
+from pydantic import BaseModel, Field, field_validator
+from transformers import PreTrainedModel
+
+__all__ = ["SpinQuantMapping", "infer_mapping_from_model"]
+
+
+class SpinQuantMapping(BaseModel):
+    embedding: str
+
+    attn_q: str
+    attn_k: str
+    attn_v: str
+    attn_o: str
+    attn_head_dim: Optional[int] = Field(default=None)
+
+    mlp_in: List[str]  # up_proj, gate_proj
+    mlp_out: List[str]  # down_proj
+
+    lm_head: str
+
+    @field_validator("mlp_in", "mlp_out", mode="before")
+    def cast_to_list(cls, value):
+        if isinstance(value, str):
+            return [value]
+
+        return value
+
+
+_default_mappings = SpinQuantMapping(
+    embedding="re:.*embed_tokens$",
+    attn_q="re:.*q_proj$",
+    attn_k="re:.*k_proj$",
+    attn_v="re:.*v_proj$",
+    attn_o="re:.*o_proj$",
+    mlp_in=["re:.*up_proj$", "re:.*gate_proj$"],
+    mlp_out="re:.*down_proj$",
+    lm_head="lm_head",
+)
+
+
+SPINQUANT_MAPPING_REGISTRY: Dict[str, SpinQuantMapping] = {
+    "LlamaForCausalLM": _default_mappings,
+}
+
+
+def infer_mapping_from_model(model: PreTrainedModel) -> SpinQuantMapping:
+    architecture = model.__class__.__name__
+    if architecture not in SPINQUANT_MAPPING_REGISTRY:
+        logger.info(
+            f"Unrecognized model architecture {architecture}. "
+            "Falling back to default mappings"
+        )
+
+    return SPINQUANT_MAPPING_REGISTRY.get(architecture, _default_mappings)
diff --git a/src/llmcompressor/modifiers/transform/spinquant/norm_mappings.py b/src/llmcompressor/modifiers/transform/spinquant/norm_mappings.py
new file mode 100644
index 000000000..0752f6986
--- /dev/null
+++ b/src/llmcompressor/modifiers/transform/spinquant/norm_mappings.py
@@ -0,0 +1,50 @@
+from typing import Dict, List
+
+from loguru import logger
+from pydantic import BaseModel, field_validator
+from transformers import PreTrainedModel
+
+__all__ = ["infer_norm_mapping_from_model"]
+
+
+class NormMapping(BaseModel):
+    norm: str
+    linears: List[str]
+
+    @field_validator("linears", mode="before")
+    def cast_to_list(cls, value):
+        if isinstance(value, str):
+            return [value]
+
+        return value
+
+
+_default_mappings = [
+    NormMapping(
+        norm="re:.*input_layernorm$",
+        linears=["re:.*q_proj$", "re:.*k_proj$", "re:.*v_proj$"],
+    ),
+    NormMapping(
+        norm="re:.*post_attention_layernorm$",
+        linears=["re:.*up_proj$", "re:.*gate_proj$"],
+    ),
+    NormMapping(
+        norm="model.norm",
+        linears=["lm_head"],
+    ),
+]
+
+NORM_MAPPING_REGISTRY: Dict[str, NormMapping] = {
+    "LlamaForCausalLM": _default_mappings,
+}
+
+
+def infer_norm_mapping_from_model(model: PreTrainedModel) -> List[NormMapping]:
+    architecture = model.__class__.__name__
+    if architecture not in NORM_MAPPING_REGISTRY:
+        logger.info(
+            f"Unrecognized model architecture {architecture}. "
+            "Falling back to default mappings"
+        )
+
+    return NORM_MAPPING_REGISTRY.get(architecture, _default_mappings)
diff --git a/src/llmcompressor/pipelines/data_free/pipeline.py b/src/llmcompressor/pipelines/data_free/pipeline.py
index 587f7ca69..7ad6d56dc 100644
--- a/src/llmcompressor/pipelines/data_free/pipeline.py
+++ b/src/llmcompressor/pipelines/data_free/pipeline.py
@@ -5,6 +5,7 @@
 
 from llmcompressor.core.session_functions import LifecycleCallbacks
 from llmcompressor.pipelines.registry import CalibrationPipeline
+from llmcompressor.utils.dev import dispatch_for_generation
 
 if TYPE_CHECKING:
     from llmcompressor.args.dataset_arguments import DatasetArguments
@@ -27,5 +28,9 @@ def __call__(
         :param dataloader: loads data for calibration
         :param dataset_args: dataset arguments relevant to pipelines
         """
+        # some ops are still performed on the model by modifiers
+        # we want those ops to occur on the GPU
+        dispatch_for_generation(model)
+
         LifecycleCallbacks.calibration_epoch_start()
         LifecycleCallbacks.calibration_epoch_end()
diff --git a/src/llmcompressor/pipelines/registry.py b/src/llmcompressor/pipelines/registry.py
index 2c1a54cf5..67d510d13 100644
--- a/src/llmcompressor/pipelines/registry.py
+++ b/src/llmcompressor/pipelines/registry.py
@@ -8,6 +8,7 @@
 
 from llmcompressor.modifiers import Modifier
 from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.modifiers.transform import SpinQuantModifier
 
 if TYPE_CHECKING:
     from llmcompressor.args.dataset_arguments import DatasetArguments
@@ -61,4 +62,8 @@ def _infer_pipeline(modifiers: List[Modifier]) -> str:
             if not config.requires_calibration_data():
                 return "datafree"
 
+        # TODO: Remove hardcode
+        if len(modifiers) == 1 and isinstance(modifiers[0], SpinQuantModifier):
+            return "datafree"
+
         return "sequential"
diff --git a/tests/llmcompressor/modeling/test_fuse.py b/tests/llmcompressor/modeling/test_fuse.py
index 005d89f99..5798f692c 100644
--- a/tests/llmcompressor/modeling/test_fuse.py
+++ b/tests/llmcompressor/modeling/test_fuse.py
@@ -1,13 +1,13 @@
 import pytest
 import torch
 
-from llmcompressor.modeling.fuse import center_embeddings, fuse_norm_linears
+from llmcompressor.modeling.fuse import fuse_norm_linears, normalize_embedding
 
 
 @pytest.mark.unit
-def test_center_embeddings():
+def test_normalize_embedding():
     embedding = torch.nn.Embedding(10, 10)
-    center_embeddings(embedding)
+    normalize_embedding(embedding)
 
     assert torch.allclose(
         embedding.weight.mean(dim=1), torch.zeros(embedding.num_embeddings), atol=1e-5
diff --git a/tests/llmcompressor/modifiers/transform/test_correctness.py b/tests/llmcompressor/modifiers/transform/test_correctness.py
new file mode 100644
index 000000000..660bab0ef
--- /dev/null
+++ b/tests/llmcompressor/modifiers/transform/test_correctness.py
@@ -0,0 +1,34 @@
+import pytest
+import torch
+from compressed_tensors.transform import apply_transform_config
+from transformers import AutoModelForCausalLM
+
+from llmcompressor.modifiers.transform.template.quip import QUIP
+
+
+@pytest.mark.parametrize(
+    "dtype,exp_max,exp_mse",
+    [
+        (
+            torch.bfloat16,
+            1.1,
+            0.012,
+        ),  # constructing and running transforms in float32 can improve to (~0.6562, ~0.0055)  # noqa: E501
+        (torch.float32, 4e-4, 2e-9),
+    ],
+)
+def test_apply_correctness(dtype, exp_max, exp_mse):
+    model = AutoModelForCausalLM.from_pretrained(
+        "meta-llama/Meta-Llama-3-8B-Instruct", device_map="cuda", torch_dtype=dtype
+    )
+
+    input = {k: v.to("cuda") for k, v in model.dummy_inputs.items()}
+    with torch.no_grad():
+        true_output = model(**input)
+
+    apply_transform_config(model, QUIP)
+    with torch.no_grad():
+        output = model(**input)
+
+    assert torch.max(true_output.logits - output.logits) <= exp_max
+    assert torch.nn.MSELoss()(output.logits, true_output.logits) <= exp_mse