Release v0.1.6

Former-commit-id: 43c8b3c3c8bfb2e32d17fb3e8b194938e37d54bd

src/llmtuner/tuner/core/adapter.py

@@ -65,7 +65,7 @@ def init_adapter(
             assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
                 "The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
 
-            if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
+            if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable): # continually fine-tuning
                 checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
             else:
                 checkpoints_to_merge = model_args.checkpoint_dir

src/llmtuner/tuner/tune.py

@@ -18,7 +18,7 @@ logger = get_logger(__name__)
 
 def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
     model_args, data_args, training_args, finetuning_args, generating_args, general_args = get_train_args(args)
-    callbacks = [LogCallback()] if callbacks is None else callbacks + [LogCallback()]
+    callbacks = [LogCallback()] if callbacks is None else callbacks
 
     if general_args.stage == "pt":
         run_pt(model_args, data_args, training_args, finetuning_args, callbacks)