diff --git a/recipes/generate.py b/recipes/generate.py
index d7ea12cc2f..fea44ddace 100644
--- a/recipes/generate.py
+++ b/recipes/generate.py
@@ -15,6 +15,7 @@
 from torchtune import config, generation, training, utils
 from torchtune.config._utils import _get_component_from_path
 from torchtune.data import ChatFormat, InstructTemplate, Message
+from torchtune.training import FullModelTorchTuneCheckpointer
 
 logger = utils.get_logger("DEBUG")
 
@@ -44,12 +45,26 @@ def __init__(self, cfg: DictConfig) -> None:
 
     def setup(self, cfg: DictConfig) -> None:
         checkpointer = config.instantiate(cfg.checkpointer)
+
+        if self._quantization_mode is not None:
+            if not isinstance(checkpointer, FullModelTorchTuneCheckpointer):
+                raise ValueError(
+                    "Quantization is only supported for models quantized and saved with the "
+                    "FullModelTorchTuneCheckpointer - please ensure you have quantized your "
+                    "model and are using the quantized weights!"
+                )
+            if "qat" in self._quantization_mode:
+                raise ValueError(
+                    "You have specified a quantizer with 'QAT' - "
+                    "QAT quantizers should only be used during quantization aware training "
+                    "and when quantizing models. Please use the corresponding post-training "
+                    "quantizer e.g. Int8DynActInt4WeightQuantizer for Int8DynActInt4WeightQATQuantizer."
+                )
+
         if self._quantization_mode is None:
             ckpt_dict = checkpointer.load_checkpoint()
         else:
             # weights_only needs to be False when loading a quantized model
-            # currently loading a quantized model is only supported with the
-            # FullModelTorchTuneCheckpointer
             ckpt_dict = checkpointer.load_checkpoint(weights_only=False)
 
         self._model = self._setup_model(
@@ -69,8 +84,11 @@ def _setup_model(
         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)
 
         # Validate model was loaded in with the expected dtype.
         training.validate_expected_param_dtype(
diff --git a/torchtune/generation/_generation.py b/torchtune/generation/_generation.py
index 9241b6061b..c2d60a7373 100644
--- a/torchtune/generation/_generation.py
+++ b/torchtune/generation/_generation.py
@@ -366,7 +366,7 @@ def generate(
         tokens, logits = custom_generate_next_token(
             model,
             input_pos=curr_input_pos,
-            x=tokens,
+            x=tokens.clone(),
             mask=curr_masks,
             temperature=temperature,
             top_k=top_k,