diff --git a/scripts/finetune.py b/scripts/finetune.py
index 7c4d865fa..ab8f068aa 100644
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -182,6 +182,9 @@ def train(
         if cfg.bf16:
             cfg.fp16 = True
         cfg.bf16 = False
+        
+    # Store inference mode into cfg when passed via args
+    cfg.inference = True if "inference" in kwargs else cfg.get("inference", False)
 
     # load the tokenizer first
     tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
@@ -189,8 +192,8 @@ def train(
     tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg)
 
     if check_not_in(
-        ["inference", "shard", "merge_lora"], kwargs
-    ):  # don't need to load dataset for these
+        ["shard", "merge_lora"], kwargs
+    ) and not cfg.inference:  # don't need to load dataset for these
         train_dataset, eval_dataset = load_prepare_datasets(
             tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
         )
@@ -216,8 +219,7 @@ def train(
         cfg.model_type,
         tokenizer,
         cfg,
-        adapter=cfg.adapter,
-        inference=("inference" in kwargs),
+        adapter=cfg.adapter
     )
 
     if "merge_lora" in kwargs and cfg.adapter is not None:
@@ -230,7 +232,7 @@ def train(
             model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
         return
 
-    if "inference" in kwargs:
+    if cfg.inference:
         logging.info("calling do_inference function")
         do_inference(cfg, model, tokenizer)
         return