diff --git a/examples/pruning/README.md b/examples/pruning/README.md
index e5abb4f3e..840f6d054 100644
--- a/examples/pruning/README.md
+++ b/examples/pruning/README.md
@@ -55,8 +55,8 @@ model = GPTModel(
 # For Megatron-LM framework, you can use the following utility function
 from megatron.training.training import evaluate_and_print_results
 
-def forward_loop(model):
-    evaluate_and_print_results(model, ...)
+def forward_loop(_):
+    evaluate_and_print_results(prefix, forward_step, train_iterator, model, ...)
 
 
 # Specify the pruning constraints (Check Support Matrix for available pruning dimensions)
@@ -66,7 +66,7 @@ export_config = {
 }
 
 
-# Run the pruning process
+# Run the pruning process (if model is a list then pass model[0] to the prune API)
 # Save minitron scores at scores_path so we can re-run pruning with different export configs without running the forward loop again
 # NOTE: Skip scores_path on re-running if you want to change the dataset and re-calibrate
 model, pruning_scores = mtp.prune(
@@ -81,7 +81,7 @@ model, pruning_scores = mtp.prune(
 If your model parameters are already sorted, you can skip the sorting step by setting `"skip_sorting": True` in `config` instead of passing `forward_loop`.
 
 > [!Note]
-> Fine-tuning / distillation is required after pruning to recover the accuracy. Please refer to pruning [fine-tuning](https://nvidia.github.io/TensorRT-Model-Optimizer/guides/3_pruning.html#pruning-fine-tuning) for more details.
+> Fine-tuning / distillation is required after pruning to recover the accuracy. Please refer to [end-to-end pruning and distillation tutorial](https://github.com/NVIDIA-NeMo/NeMo/tree/main/tutorials/llm/qwen/pruning-distillation) for more details.
 
 ## Support Matrix
 
diff --git a/modelopt/torch/nas/conversion.py b/modelopt/torch/nas/conversion.py
index be5aff0c1..a93c209fb 100644
--- a/modelopt/torch/nas/conversion.py
+++ b/modelopt/torch/nas/conversion.py
@@ -26,7 +26,9 @@
 NASModeRegistry = _ModeRegistryCls("nas")
 
 
-def convert(model: ModelLike, mode: ModeLike) -> nn.Module:
+def convert(
+    model: ModelLike, mode: ModeLike, registry: _ModeRegistryCls = NASModeRegistry
+) -> nn.Module:
     """Convert a regular PyTorch model into a model that supports design space optimization.
 
     Args:
@@ -84,7 +86,7 @@ def convert(model: ModelLike, mode: ModeLike) -> nn.Module:
         #. Use ``*`` as a wildcard matching any layer.
     """
     # apply mode and handle model-like object with wrapper
-    return apply_mode(model, mode, registry=NASModeRegistry)
+    return apply_mode(model, mode, registry=registry)
 
 
 def export(model: nn.Module, strict: bool = True, calib: bool = False) -> nn.Module:
diff --git a/modelopt/torch/prune/pruning.py b/modelopt/torch/prune/pruning.py
index 322e7e0c0..cdc4e7d8f 100644
--- a/modelopt/torch/prune/pruning.py
+++ b/modelopt/torch/prune/pruning.py
@@ -20,7 +20,6 @@
 from torch import nn
 
 import modelopt.torch.nas as mtn
-from modelopt.torch.opt.conversion import apply_mode
 from modelopt.torch.opt.mode import ModeLike, _ModeRegistryCls
 from modelopt.torch.opt.searcher import ConstraintsDict, SearchConfig
 
@@ -199,8 +198,8 @@ def prune(
         search algorithm. The returned subnet is thus a reference to the same model instance as the
         input model.
     """
-    # apply prune mode(s) to model
-    model = apply_mode(model, mode, registry=PruneModeRegistry)
+    # apply prune mode(s) to model and convert it to DynamicModule
+    model = mtn.convert(model, mode, registry=PruneModeRegistry)
 
     # now run the search and return the result
     return mtn.search(