support DPO training (2305.18290)

Former-commit-id: 6d98de148e4af63a7028dfaeb6cf86eb56a4488f

src/llmtuner/hparams/model_args.py

@@ -1,12 +1,11 @@
 import torch
 from typing import Literal, Optional
 from dataclasses import dataclass, field
-from huggingface_hub.hf_api import HfFolder
 
 
 @dataclass
 class ModelArguments:
-    """
+    r"""
     Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
     """
     model_name_or_path: str = field(
@@ -64,12 +63,11 @@ class ModelArguments:
         default=False,
         metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
     )
-    hf_hub_token  : Optional[str] = field(
+    hf_auth_token: Optional[str] = field(
         default=None,
-        metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
+        metadata={"help": "Auth token to log in with Hugging Face Hub."}
     )
 
-
     def __post_init__(self):
         if self.checkpoint_dir is not None: # support merging multiple lora weights
             self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
@@ -77,5 +75,6 @@ class ModelArguments:
         if self.quantization_bit is not None:
             assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
 
-        if self.use_auth_token == True and self.hf_hub_token != None:
-            HfFolder.save_token(self.hf_hub_token)
+        if self.use_auth_token == True and self.hf_auth_token is not None:
+            from huggingface_hub.hf_api import HfFolder # lazy load
+            HfFolder.save_token(self.hf_auth_token)