diff --git a/src/instructlab/training/main_ds.py b/src/instructlab/training/main_ds.py
index 6464b1a7..b56d5b85 100644
--- a/src/instructlab/training/main_ds.py
+++ b/src/instructlab/training/main_ds.py
@@ -445,6 +445,7 @@ def train(args, model, tokenizer, train_loader, grad_accum, metric_logger):
                     model,
                     tokenizer,
                     global_step * args.samples_per_gpu * world_size,
+                    is_lora=bool(args.lora_r),
                 )
 
             if (
diff --git a/src/instructlab/training/utils.py b/src/instructlab/training/utils.py
index 4ea8ac77..b68988f9 100644
--- a/src/instructlab/training/utils.py
+++ b/src/instructlab/training/utils.py
@@ -1,6 +1,7 @@
 # Standard
+from collections import OrderedDict
 from contextlib import contextmanager
-from copy import copy
+from copy import copy, deepcopy
 from functools import partial
 from pathlib import Path
 from tempfile import TemporaryDirectory
@@ -611,7 +612,24 @@ def log_rank_0(msg, include_caller=False, rank=None, to_print=False):
         # print(msg)
 
 
-def save_hf_format_ds(args, model, tokenizer, samples_seen, convert_granite=True):
+def _copy_no_lora_dict(state_dict):
+    cleaned_state_dict = OrderedDict()
+    for param_tensor in state_dict:
+        if not "lora" in param_tensor:
+            cleaned_state_dict[
+                param_tensor.replace(".base_layer", "").replace("base_model.model.", "")
+            ] = deepcopy(state_dict[param_tensor]).cpu()
+    return cleaned_state_dict
+
+
+def save_hf_format_ds(
+    args,
+    model,
+    tokenizer,
+    samples_seen,
+    convert_granite=True,
+    is_lora=False,
+):
     model_to_save = model.module
     log_rank_0(
         f"\033[93mSaving model in huggingface format at samples_seen: {samples_seen}\033[0m",
@@ -628,7 +646,12 @@ def save_hf_format_ds(args, model, tokenizer, samples_seen, convert_granite=True
         WEIGHTS_NAME = "pytorch_model.bin"
     output_dir = Path(args.output_dir) / "hf_format" / f"samples_{samples_seen}"
     if torch.distributed.get_rank() == 0:
+        if is_lora:
+            model_to_save.merge_adapter()
+
         model_state = model_to_save.state_dict()
+        if is_lora:
+            model_state = _copy_no_lora_dict(model_state)
         output_dir.mkdir(parents=True, exist_ok=True)
         output_model_file = output_dir / WEIGHTS_NAME
         output_config_file = output_dir / CONFIG_NAME
@@ -658,6 +681,9 @@ def save_hf_format_ds(args, model, tokenizer, samples_seen, convert_granite=True
             tmp_conf.to_json_file(str(output_config_file))
             tokenizer.save_pretrained(str(output_dir))
 
+        if is_lora:
+            model_to_save.unmerge_adapter()
+
     dist.barrier()
     log_rank_0(f"\033[93mModel saved in {output_dir}\033[0m", to_print=True)
     log_rank_0(f"saving took {time.time() - start} seconds")