tiny fix

README.md

@@ -21,13 +21,13 @@
 ## Supported Training Approaches
 
 - [(Continually) pre-training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
-  - Full-parameter training
-  - Partial-parameter training
+  - Full-parameter tuning
+  - Partial-parameter tuning
   - [LoRA](https://arxiv.org/abs/2106.09685)
   - [QLoRA](https://arxiv.org/abs/2305.14314)
 - [Supervised fine-tuning](https://arxiv.org/abs/2109.01652)
-  - Full-parameter training
-  - Partial-parameter training
+  - Full-parameter tuning
+  - Partial-parameter tuning
   - [LoRA](https://arxiv.org/abs/2106.09685)
   - [QLoRA](https://arxiv.org/abs/2305.14314)
 - [RLHF](https://arxiv.org/abs/2203.02155)

src/utils/common.py

@@ -261,8 +261,8 @@ def prepare_args(
     if training_args.do_predict and (not training_args.predict_with_generate):
         raise ValueError("Please enable `predict_with_generate` to save model predictions.")
 
-    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
-        raise ValueError("Quantization is only compatible with the LoRA method.")
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type == "full":
+        raise ValueError("Quantization is incompatible with the full-parameter tuning.")
 
     if model_args.quantization_bit is not None and (not training_args.do_train):
         logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")

src/utils/config.py

@@ -148,7 +148,8 @@ class DataTrainingArguments:
 
     def __post_init__(self): # support mixing multiple datasets
         dataset_names = [ds.strip() for ds in self.dataset.split(",")]
-        dataset_info = json.load(open(os.path.join(self.dataset_dir, "dataset_info.json"), "r"))
+        with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f:
+            dataset_info = json.load(f)
 
         self.dataset_list: List[DatasetAttr] = []
         for name in dataset_names: