fix #944

Former-commit-id: 032245647848aaa4167086636b6c985268c5fee3

src/llmtuner/hparams/data_args.py

@@ -42,12 +42,16 @@ class DataArguments:
         default="train",
         metadata={"help": "Which dataset split to use for training and evaluation."}
     )
+    cutoff_len: Optional[int] = field(
+        default=1024,
+        metadata={"help": "The maximum length of the model inputs after tokenization."}
+    )
     streaming: Optional[bool] = field(
         default=False,
         metadata={"help": "Enable streaming mode."}
     )
     buffer_size: Optional[int] = field(
-        default=1024,
+        default=16384,
         metadata={"help": "Size of the buffer to randomly sample examples from in streaming mode."}
     )
     mix_strategy: Optional[Literal["concat", "interleave_under", "interleave_over"]] = field(
@@ -66,14 +70,6 @@ class DataArguments:
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."}
     )
-    max_source_length: Optional[int] = field(
-        default=512,
-        metadata={"help": "The maximum total input sequence length after tokenization."}
-    )
-    max_target_length: Optional[int] = field(
-        default=512,
-        metadata={"help": "The maximum total output sequence length after tokenization."}
-    )
     max_samples: Optional[int] = field(
         default=None,
         metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."}

src/llmtuner/hparams/model_args.py

@@ -63,18 +63,10 @@ class ModelArguments:
         default=None,
         metadata={"help": "Auth token to log in with Hugging Face Hub."}
     )
-    compute_dtype: Optional[torch.dtype] = field(
-        default=None,
-        metadata={"help": "Used in quantization configs. Do not specify this argument manually."}
-    )
-    model_max_length: Optional[int] = field(
-        default=None,
-        metadata={"help": "Used in rope scaling. Do not specify this argument manually."}
-    )
 
     def __post_init__(self):
-        if self.compute_dtype is not None or self.model_max_length is not None:
-            raise ValueError("These arguments cannot be specified.")
+        self.compute_dtype = None
+        self.model_max_length = None
 
         if self.checkpoint_dir is not None: # support merging multiple lora weights
             self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]