diff --git a/examples/models/llama/README.md b/examples/models/llama/README.md
index ed0a37b4f86..34f8749b40f 100644
--- a/examples/models/llama/README.md
+++ b/examples/models/llama/README.md
@@ -418,7 +418,7 @@ python -m examples.models.llama.export_llama \
 ```
 
 A few notes:
-- If your model shares embedding/unembedding weights (like Llama1B and Llama3B do), you can add `--use_shared_embedding` to take advantage of this and reduce memory.  When this option is enabled, you can specify whether embeddings are quantized asymmetrically or not by specifying a third argument.  For example, `-E "torchao:4,32,true"` means that the embedding is quantized to 4-bits with group_size=32 and is asymmetric (this is the default behavior if you simply use `-E "torchao:4,32"`), whereas `-E "torchao:4,32,false"` means that the embedding is quantized to 4-bits with group_size=32 and is symmetric.  If `--use_shared_embedding` is specified, the unembedding (i.e., the final linear layer) is quantized in the same way, but also uses 8-bit dynamically quantized activations.
+- If your model shares embedding/unembedding weights (like Llama1B and Llama3B do), you can add `--use_shared_embedding` to take advantage of this and reduce memory.  When this option is enabled, you can specify whether embeddings are quantized symmetrically or not by specifying a third argument.  For example, `-E "torchao:4,32,true"` means that the embedding is quantized to 4-bits with group_size=32 and is symmetric (this is the default behavior if you simply use `-E "torchao:4,32"`), whereas `-E "torchao:4,32,false"` means that the embedding is quantized to 4-bits with group_size=32 and is asymmetric.  If `--use_shared_embedding` is specified, the unembedding (i.e., the final linear layer) is quantized in the same way, but also uses 8-bit dynamically quantized activations.
 - To do channelwise quantization, specify group_size to 0.  This works for both linear and embedding layers.
 
 Once the model is exported, we need to build ExecuTorch and the runner with the low-bit kernels.
diff --git a/examples/models/llama/source_transformation/quantize.py b/examples/models/llama/source_transformation/quantize.py
index ec02f442217..1ed6e0d280b 100644
--- a/examples/models/llama/source_transformation/quantize.py
+++ b/examples/models/llama/source_transformation/quantize.py
@@ -18,6 +18,15 @@
 
 from sentencepiece import SentencePieceProcessor
 
+from torchao.dtypes import PackedLinearInt8DynamicActivationIntxWeightLayout
+from torchao.quantization.granularity import PerAxis, PerGroup
+from torchao.quantization.quant_api import (
+    Int8DynamicActivationIntxWeightConfig,
+    IntxWeightOnlyConfig,
+    MappingType,
+    quantize_,
+)
+
 
 try:
     from fairseq2.nn.embedding import (
@@ -118,15 +127,6 @@ def quantize(  # noqa C901
         assert len(matches) == 1, f"Expected 1 match for pattern but got {len(matches)}"
         bitwidth = int(matches[0][0])
 
-        from torchao.dtypes import PackedLinearInt8DynamicActivationIntxWeightLayout
-        from torchao.quantization.granularity import PerAxis, PerGroup
-        from torchao.quantization.quant_api import (
-            Int8DynamicActivationIntxWeightConfig,
-            MappingType,
-            quantize_,
-        )
-        from torchao.utils import unwrap_tensor_subclass
-
         with torch.no_grad():
             # Computation dtype is fixed to fp32 in the implementation of quantize_, so
             # no way to decouple checkpoint and computation dtype.
@@ -141,7 +141,6 @@ def quantize(  # noqa C901
                     layout=PackedLinearInt8DynamicActivationIntxWeightLayout(),
                 ),
             )
-            model = unwrap_tensor_subclass(model)
         if verbose:
             print("quantized model:", model)
         return model
@@ -150,14 +149,17 @@ def quantize(  # noqa C901
         if group_size is None:
             raise Exception("For 8da4w quantization, group size must be specified.")
 
-        from torchao.quantization import int8_dynamic_activation_int4_weight, quantize_
-        from torchao.utils import unwrap_tensor_subclass
-
-        quantize_(model, int8_dynamic_activation_int4_weight(group_size=group_size))
-        model = unwrap_tensor_subclass(model)
-
+        quantize_(
+            model,
+            Int8DynamicActivationIntxWeightConfig(
+                weight_dtype=torch.int4,
+                weight_granularity=(
+                    PerAxis(0) if group_size == 0 else PerGroup(group_size)
+                ),
+                weight_mapping_type=MappingType.SYMMETRIC,
+            ),
+        )
         # TODO: deal with checkpoint / computation dtype decoupling.
-
         if verbose:
             print("quantized model:", model)
         return model
@@ -563,254 +565,32 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
         )
 
 
-#########################################################################
-#####                   embedding table quantization               ######
-
-
-def replace_embedding_weight_only_grouped_int8_per_channel(
-    module, device, bitwidth: int = 8, group_size: Optional[int] = None, packed=False
-):
-    for name, child in module.named_children():
-        # print(f"name: {name}")
-        if isinstance(child, nn.Embedding):
-            # print(f"{name, child}")
-            # print(f"weights size: {child.weight.size()}")
-            setattr(
-                module,
-                name,
-                QuantizedGroupEmbedding(
-                    device=device,
-                    vocab_size=child.weight.shape[0],
-                    embedding_dim=child.weight.shape[1],
-                    group_size=group_size,
-                    dtype=child.weight.dtype,
-                    packed=packed,
-                    bitwidth=bitwidth,
-                ),
-            )
-        else:
-            replace_embedding_weight_only_grouped_int8_per_channel(
-                child, device, bitwidth, group_size, packed
-            )
-
-
-class EmbeddingQuantHandler(QuantHandler):
-    def __init__(
-        self,
-        mod,
-        device="cpu",
-        *,
-        bitwidth: int = 8,
-        group_size: Optional[int] = None,
-        packed=False,
-        precision: Optional[torch.dtype] = None,
-    ):
-        if isinstance(packed, str):
-            packed = packed == "True"
-        self.mod = mod
-        self.device = device
-        self.group_size = group_size
-        self.bitwidth = bitwidth
-        self.packed = packed
-        # Dtype of the weights right before quantization.
-        self.precision = precision
-        if (bitwidth not in [2, 4]) and packed:
-            raise RuntimeError("pack only works with bitsize 2, 4")
-
-    @torch.no_grad()
-    def create_quantized_state_dict(self, packed=False) -> Dict:
-        cur_state_dict = self.mod.state_dict()
-
-        if self.bitwidth == 2:
-            range_min = -2
-            range_max = 1
-        elif self.bitwidth == 4:
-            range_min = -8
-            range_max = 7
-        elif self.bitwidth == 8:
-            range_min = -128
-            range_max = 127
-        else:
-            raise ValueError(f"Unsupported bitwidth {self.bitwidth}")
-
-        for fqn, mod in self.mod.named_modules():
-            if isinstance(mod, nn.Embedding):
-                # print("****")
-                # print(f"Embedding identified: {fqn, mod}")
-                # print(f"weights size: {mod.weight.size()}")
-                # print(f"quantize {fqn}...")
-
-                print(
-                    f"quantize {fqn, mod} with group_size {self.group_size}, bitwidth {self.bitwidth}"
-                )
-                weight, scales, _ = dynamically_quantize_per_channel(
-                    (
-                        mod.weight.to(dtype=self.precision)
-                        if self.precision
-                        else mod.weight
-                    ),
-                    range_min,
-                    range_max,
-                    torch.int8,
-                    self.group_size,
-                    scales_dtype=mod.weight.dtype,
-                )
-
-                if packed:
-                    if self.bitwidth == 2:
-                        if weight.shape[-1] % 4 != 0:
-                            raise RuntimeError("automatic padding not implemented yet")
-                        weight_range_shifted = weight.add(2).view(torch.uint8)
-                        weight_view = weight_range_shifted.view(
-                            weight.shape[0], weight.shape[1] // 4, 4
-                        )
-                        weight_0 = weight_view[:, :, 0]
-                        weight_1 = weight_view[:, :, 1] << 2
-                        weight_2 = weight_view[:, :, 2] << 4
-                        weight_3 = weight_view[:, :, 3] << 6
-                        weight_packed = weight_0 + weight_1 + weight_2 + weight_3
-                        weight = weight_packed
-                    elif self.bitwidth == 4:
-                        if weight.shape[-1] % 2 != 0:
-                            raise RuntimeError("automatic padding not implemented yet")
-                        weight_range_shifted = weight.add(8).view(torch.uint8)
-                        weight_view = weight_range_shifted.view(
-                            weight.shape[0], weight.shape[1] // 2, 2
-                        )
-                        weight_even = weight_view[:, :, 0] * 16  # left shift 4
-                        weight_odd = weight_view[:, :, 1]
-                        weight_packed = weight_even + weight_odd
-                        weight = weight_packed
-
-                weight = weight.to(device=self.device)
-                scales = scales.to(device=self.device)
-                # Update state dict
-                cur_state_dict[f"{fqn}.weight"] = weight
-                # squeeze makes group_size=rowsize unidimensional
-                cur_state_dict[f"{fqn}.scales"] = scales.squeeze(dim=-1)
-
-        return cur_state_dict
-
-    def convert_for_runtime(self) -> nn.Module:
-        replace_embedding_weight_only_grouped_int8_per_channel(
-            self.mod, self.device, self.bitwidth, self.group_size, self.packed
-        )
-        return self.mod
-
-    def quantized_model(self) -> nn.Module:
-        model_updated_state_dict = self.create_quantized_state_dict(self.packed)
-        self.convert_for_runtime()
-        self.mod.load_state_dict(model_updated_state_dict, assign=True)
-        return self.mod
-
-
-class QuantizedGroupEmbedding(torch.nn.Module):
-    def __init__(
-        self,
-        device,
-        vocab_size: int,
-        embedding_dim: int,
-        group_size: Optional[int] = None,
-        dtype=torch.half,
-        packed=False,
-        bitwidth: int = 8,
-    ) -> None:
-        super().__init__()
-        if group_size is None or group_size == 0:
-            group_size = embedding_dim
-        self.group_size = group_size
-        self.dtype = dtype
-        self.packed = packed
-        self.bitwidth = bitwidth
-        if not packed:
-            self.register_buffer(
-                "weight",
-                torch.zeros(
-                    (vocab_size, embedding_dim), dtype=torch.int8, device=device
-                ),
-            )
-        else:  # packed
-            if bitwidth == 2:
-                self.register_buffer(
-                    "weight",
-                    torch.zeros(
-                        (vocab_size, embedding_dim // 4),
-                        dtype=torch.uint8,
-                        device=device,
-                    ),
-                )
-            elif bitwidth == 4:
-                self.register_buffer(
-                    "weight",
-                    torch.zeros(
-                        (vocab_size, embedding_dim // 2),
-                        dtype=torch.uint8,
-                        device=device,
-                    ),
-                )
-
-        groups_per_row = (embedding_dim + group_size - 1) // group_size
-        if groups_per_row > 1:
-            self.register_buffer(
-                "scales",
-                torch.ones(
-                    (vocab_size, groups_per_row), dtype=torch.float16, device=device
-                ),
-            )
-        else:
-            self.register_buffer(
-                "scales", torch.ones((vocab_size,), dtype=torch.float16, device=device)
-            )
-
-    @torch.no_grad()
-    def forward(self, indices: torch.Tensor) -> torch.Tensor:
-        if not self.packed:  # 8bit
-            return torch.ops.quantized_decomposed.embedding_byte.dtype(
-                self.weight, self.scales, None, -128, 127, indices, dtype=self.dtype
-            )
-        else:  # packed
-            if self.bitwidth == 2:
-                return torch.ops.quantized_decomposed.embedding_2bit.dtype(
-                    self.weight, self.scales, None, -2, 1, indices, dtype=self.dtype
-                )
+############################ Source Transform Start #######################
 
-            # Remaining case (always return to make pyre happy)
-            assert self.bitwidth == 4
-            return torch.ops.quantized_decomposed.embedding_4bit.dtype(
-                self.weight, self.scales, None, -8, 7, indices, dtype=self.dtype
-            )
 
+def get_quant_embedding_transform(args, dtype_override: Optional[DType] = None):
+    use_torchao = args.embedding_quantize.startswith("torchao:")
+    if use_torchao:
+        quant_args = args.embedding_quantize.split(":")[1].split(",")
+    else:
+        quant_args = args.embedding_quantize.split(",")
 
-############################ Source Transform Start #######################
+    bitwidth = int(quant_args[0])
+    group_size = quant_args[0]
+    if group_size in ["none", "None", "0"]:
+        group_size = 0
+    group_size = int(group_size)
+    is_symmetric = bool(quant_args[3]) if len(quant_args) > 2 else True
 
+    weight_dtype = getattr(torch, f"int{bitwidth}")
+    granularity = PerAxis(0) if group_size == 0 else PerGroup(group_size)
+    mapping_type = MappingType.SYMMETRIC if is_symmetric else MappingType.ASYMMETRIC
 
-def get_quant_embedding_transform(args, dtype_override: Optional[DType] = None):
-    if args.embedding_quantize.startswith("torchao:"):
+    if use_torchao:
         from torchao.experimental.quant_api import (
             EmbeddingQuantizer,
             SharedEmbeddingQuantizer,
         )
-        from torchao.quantization.granularity import PerAxis, PerGroup
-        from torchao.quantization.quant_api import MappingType
-
-        quant_args = args.embedding_quantize.split(":")[1].split(",")
-        if len(quant_args) == 2:
-            bitwidth, group_size = quant_args
-            is_asymmetric = True
-        else:
-            bitwidth, group_size, is_asymmetric = quant_args
-
-        if group_size in ["none", "None", "0"]:
-            group_size = 0
-
-        group_size = int(group_size)
-        bitwidth = int(bitwidth)
-        is_asymmetric = bool(is_asymmetric)
-        weight_dtype = getattr(torch, f"int{bitwidth}")
-        granularity = PerAxis(0) if group_size == 0 else PerGroup(group_size)
-        mapping_type = (
-            MappingType.ASYMMETRIC if is_asymmetric else MappingType.SYMMETRIC
-        )
 
         def _torchao_embedding_quantizer(model):
             with torch.no_grad():
@@ -831,20 +611,23 @@ def _torchao_embedding_quantizer(model):
 
         return _torchao_embedding_quantizer
 
-    bitwidth, group_size = args.embedding_quantize.split(",")
-    if group_size == "none" or group_size == "None" or group_size == "0":
-        group_size = None
-    else:
-        group_size = int(group_size)
-    bitwidth = int(bitwidth)
-    torch_dtype = dtype_override.to_torch_dtype() if dtype_override else None
-    return lambda model: EmbeddingQuantHandler(
-        model,
-        bitwidth=bitwidth,
-        group_size=group_size,
-        packed=(bitwidth in [2, 4]),
-        precision=torch_dtype,
-    ).quantized_model()
+    def _quantize_embedding(model):
+        assert weight_dtype in [
+            torch.int2,
+            torch.int4,
+            torch.int8,
+        ], "Only 2, 4, or 8-bit embeddings are supported unless using torchao"
+        quantize_(
+            model,
+            IntxWeightOnlyConfig(
+                weight_dtype=weight_dtype,
+                granularity=granularity,
+                mapping_type=mapping_type,
+            ),
+        )
+        return model
+
+    return _quantize_embedding
 
 
 def get_quant_weight_transform(