diff --git a/scripts/finetune.py b/scripts/finetune.py index 3222afd81..49bd505ce 100644 --- a/scripts/finetune.py +++ b/scripts/finetune.py @@ -158,7 +158,7 @@ def train( cfg_keys = cfg.keys() for k, _ in kwargs.items(): # if not strict, allow writing to cfg even if it's not in the yml already - if k in cfg_keys or cfg.strict is False: + if k in cfg_keys or not cfg.strict: # handle booleans if isinstance(cfg[k], bool): cfg[k] = bool(kwargs[k]) @@ -198,9 +198,9 @@ def train( logging.info(f"loading tokenizer... {tokenizer_config}") tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg) - if check_not_in( - ["shard", "merge_lora"], kwargs - ) and not cfg.inference: # don't need to load dataset for these + if ( + check_not_in(["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 ) @@ -226,7 +226,7 @@ def train( cfg.model_type, tokenizer, cfg, - adapter=cfg.adapter + adapter=cfg.adapter, ) if "merge_lora" in kwargs and cfg.adapter is not None: diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py index 67facd607..3a87392fc 100644 --- a/src/axolotl/utils/models.py +++ b/src/axolotl/utils/models.py @@ -77,14 +77,9 @@ def load_tokenizer( def load_model( - base_model, - base_model_config, - model_type, - tokenizer, - cfg, - adapter="lora" + base_model, base_model_config, model_type, tokenizer, cfg, adapter="lora" ): - # type: (str, str, str, AutoTokenizer, DictDefault, Optional[str], bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]] + # type: (str, str, str, AutoTokenizer, DictDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]] """ Load a model from a base model and a model type. """