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Fix typo in common.py 

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Jinghuan Shang 2023-07-11 18:03:53 -04:00 committed by GitHub
parent b2f7cb4465
commit dc1e8b7181
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1 changed files with 5 additions and 5 deletions
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src/utils/common.py
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@ -97,7 +97,7 @@ def _init_adapter(
if finetuning_args.finetuning_type == "lora":
logger.info("Fine-tuning method: LoRA")
lastest_checkpoint = None
latest_checkpoint = None
if model_args.checkpoint_dir is not None:
assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)), \
@ -106,7 +106,7 @@ def _init_adapter(
"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
checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
else:
checkpoints_to_merge = model_args.checkpoint_dir
@ -117,10 +117,10 @@ def _init_adapter(
if len(checkpoints_to_merge) > 0:
logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
if lastest_checkpoint is not None: # resume lora training or quantized inference
model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=is_trainable)
if latest_checkpoint is not None: # resume lora training or quantized inference
model = PeftModel.from_pretrained(model, latest_checkpoint, is_trainable=is_trainable)
if is_trainable and lastest_checkpoint is None: # create new lora weights while training
if is_trainable and latest_checkpoint is None: # create new lora weights while training
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=False,