diff --git a/transformerlab/plugins/mlx_lora_trainer/main.py b/transformerlab/plugins/mlx_lora_trainer/main.py
index d34cd4c21..abfd6c385 100644
--- a/transformerlab/plugins/mlx_lora_trainer/main.py
+++ b/transformerlab/plugins/mlx_lora_trainer/main.py
@@ -46,6 +46,10 @@ def train_mlx_lora():
     chat_column = tlab_trainer.params.get("chatml_formatted_column", "messages")
     formatting_template = tlab_trainer.params.get("formatting_template", None)
 
+    # Check if we are starting from a checkpoint:
+    restart_from_checkpoint = tlab_trainer.params.get("restart_from_checkpoint", None)
+    print("Restarting from checkpoint:", restart_from_checkpoint)
+
     if num_train_epochs is not None and num_train_epochs != "" and int(num_train_epochs) >= 0:
         if num_train_epochs == 0:
             print(
@@ -109,6 +113,11 @@ def train_mlx_lora():
     if not os.path.exists(adaptor_output_dir):
         os.makedirs(adaptor_output_dir)
 
+    # If checkpointing is enabled, then set the path to be the checkpoint name in the adaptor output directory
+    if restart_from_checkpoint:
+        checkpoint_path = os.path.join(adaptor_output_dir, restart_from_checkpoint)
+        print("Using checkpoint output directory:", checkpoint_path)
+
     # Get Python executable (from venv if available)
     python_executable = get_python_executable(plugin_dir)
     env = os.environ.copy()
@@ -142,6 +151,9 @@ def train_mlx_lora():
         str(save_every),
     ]
 
+    if restart_from_checkpoint:
+        popen_command.extend(["--resume-adapter-file", checkpoint_path])
+
     # If a config file has been created then include it
     if config_file:
         popen_command.extend(["--config", config_file])
diff --git a/transformerlab/routers/tasks.py b/transformerlab/routers/tasks.py
index 333529a4f..c8f69eacb 100644
--- a/transformerlab/routers/tasks.py
+++ b/transformerlab/routers/tasks.py
@@ -223,6 +223,8 @@ async def queue_task(task_id: int, input_override: str = "{}", output_override:
     if not isinstance(output_override, dict):
         output_override = json.loads(output_override)
 
+    print(f"Input override: {input_override}")
+
     if not isinstance(task_to_queue["config"], dict):
         task_to_queue["config"] = json.loads(task_to_queue["config"])