diff --git a/src/brevitas_examples/llm/main.py b/src/brevitas_examples/llm/main.py
index 05d9bcb47..955f2a749 100644
--- a/src/brevitas_examples/llm/main.py
+++ b/src/brevitas_examples/llm/main.py
@@ -382,19 +382,23 @@ def main(args):
 
     dict_hooks = dict()
 
-    def update_params_post_init(module):
-        update_internal_dict(module)
-
+    # When offloading to CPU + GPU, the CPU scale factors must be updated
+    # before we move them back to the meta device.
+    # If we don't, we lose the new value but the internal flag "init_done" is True, thus we will use the wrong scale.
+    # To do this, we attach a "hook" to the post_forward function, called before the post_forward
+    # The function will update the dict with the initialized scales
     for m in model.modules():
         if hasattr(m, '_hf_hook'):
             if m._hf_hook.weights_map is not None:
+                # We store the original function to be restored later
                 dict_hooks[m] = m._hf_hook.post_forward
-                new_funct = functools.partial(update_params_post_init, m)
+                new_funct = functools.partial(update_internal_dict, m)
                 m._hf_hook.post_forward = hooked_on_a_function(m._hf_hook.post_forward, new_funct)
 
     with torch.no_grad():
         model(**calibration_loader[0])
 
+    # We restore the original behaviour of the post-forward. 
     for k, v in dict_hooks.items():
         k._hf_hook.post_forward = v