Fix strict and Lint

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:

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.
     """