diff --git a/recipes/eleuther_eval.py b/recipes/eleuther_eval.py
index 4b80e132b..60cb83ff5 100644
--- a/recipes/eleuther_eval.py
+++ b/recipes/eleuther_eval.py
@@ -222,11 +222,15 @@ def _setup_model(
     ) -> nn.Module:
         with training.set_default_dtype(self._dtype), self._device:
             model = config.instantiate(model_cfg)
+
         if self._quantization_mode is not None:
             model = self._quantizer.quantize(model)
             model = model.to(device=self._device, dtype=self._dtype)
-
-        model.load_state_dict(model_state_dict)
+            for k, v in model_state_dict.items():
+                model_state_dict[k] = v.to(self._device)
+            model.load_state_dict(model_state_dict, assign=True)
+        else:
+            model.load_state_dict(model_state_dict)
 
         # Put model in eval mode.
         # Note: This will not disable the dropout applied in SDPA,
diff --git a/torchtune/training/quantization.py b/torchtune/training/quantization.py
index 8d734b010..7046f90b6 100644
--- a/torchtune/training/quantization.py
+++ b/torchtune/training/quantization.py
@@ -6,8 +6,23 @@
 
 from typing import Callable, Optional
 
+from torchao.quantization import int8_dynamic_activation_int4_weight, quantize_
+from torchao.quantization.prototype.qat import (
+    disable_8da4w_fake_quant,
+    enable_8da4w_fake_quant,
+    Int8DynActInt4WeightQATQuantizer,
+)
+from torchao.quantization.prototype.qat._module_swap_api import (
+    disable_8da4w_fake_quant_module_swap,
+    enable_8da4w_fake_quant_module_swap,
+    Int8DynActInt4WeightQATQuantizerModuleSwap,
+)
+
+
 __all__ = [
     "get_quantizer_mode",
+    "Int8DynActInt4WeightQuantizer",
+    "Int8DynActInt4WeightQATQuantizer",
 ]
 
 
@@ -16,47 +31,47 @@
 _quantizer_mode_to_enable_fake_quant = {}
 
 
-from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
+# ========================================================
+# int8 dynamic activations + int4 weight tensor subclass |
+# ========================================================
 
-__all__.append("Int8DynActInt4WeightQuantizer")
-_quantizer_to_mode[Int8DynActInt4WeightQuantizer] = "8da4w"
 
+class Int8DynActInt4WeightQuantizer:
+    """
+    Quantizer for applying int8 per token dynamic activation + int4
+    per group weight quantization to linear layers in the model.
+    """
+
+    def __init__(self, groupsize: int = 256):
+        self.groupsize = groupsize
+
+    def quantize(self, model):
+        quantize_fn = int8_dynamic_activation_int4_weight(self.groupsize)
+        quantize_(model, quantize_fn)
+        return model
 
-from torchao.quantization.prototype.qat import (
-    disable_8da4w_fake_quant,
-    enable_8da4w_fake_quant,
-    Int8DynActInt4WeightQATQuantizer,
-)
 
-__all__.append("Int8DynActInt4WeightQATQuantizer")
+_quantizer_to_mode[Int8DynActInt4WeightQuantizer] = "8da4w"
 _quantizer_to_mode[Int8DynActInt4WeightQATQuantizer] = "8da4w-qat"
 _quantizer_mode_to_disable_fake_quant["8da4w-qat"] = disable_8da4w_fake_quant
 _quantizer_mode_to_enable_fake_quant["8da4w-qat"] = enable_8da4w_fake_quant
 
-try:
-    # Note: QAT tensor subclass implementation in torchao only works
-    # with FSDP2 today. For other distribution strategies like DDP and
-    # FSDP1, users will need to fall back to the old module swap flow.
-    # TODO: remove this try catch once we upgrade to torchao 0.5.0
-
-    from torchao.quantization.prototype.qat._module_swap_api import (
-        disable_8da4w_fake_quant_module_swap,
-        enable_8da4w_fake_quant_module_swap,
-        Int8DynActInt4WeightQATQuantizerModuleSwap,
-    )
-
-    __all__.append("Int8DynActInt4WeightQATQuantizerModuleSwap")
-    _quantizer_to_mode[
-        Int8DynActInt4WeightQATQuantizerModuleSwap
-    ] = "8da4w-qat-module-swap"
-    _quantizer_mode_to_disable_fake_quant[
-        "8da4w-qat-module-swap"
-    ] = disable_8da4w_fake_quant_module_swap
-    _quantizer_mode_to_enable_fake_quant[
-        "8da4w-qat-module-swap"
-    ] = enable_8da4w_fake_quant_module_swap
-except ImportError:
-    pass
+
+# ====================================================
+# int8 dynamic activations + int4 weight module swap |
+# ====================================================
+
+# Note: QAT tensor subclass implementation in torchao only works
+# with FSDP2 today. For other distribution strategies like DDP and
+# FSDP1, users will need to fall back to the old module swap flow.
+__all__.append("Int8DynActInt4WeightQATQuantizerModuleSwap")
+_quantizer_to_mode[Int8DynActInt4WeightQATQuantizerModuleSwap] = "8da4w-qat-module-swap"
+_quantizer_mode_to_disable_fake_quant[
+    "8da4w-qat-module-swap"
+] = disable_8da4w_fake_quant_module_swap
+_quantizer_mode_to_enable_fake_quant[
+    "8da4w-qat-module-swap"
+] = enable_8da4w_fake_quant_module_swap
 
 
 def get_quantizer_mode(quantizer: Optional[Callable]) -> Optional[str]: