修改了finetune中的默认模型的错误

finetune/finetune.py

@@ -6,12 +6,13 @@ from typing import Dict, Optional
 import torch
 import transformers
 from torch.utils.data import Dataset
-from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+from transformers import (AutoModelForCausalLM, AutoTokenizer, Trainer,
+                          TrainingArguments)
 
 
 @dataclass
 class ModelArguments:
-    model_name_or_path: Optional[str] = field(default="baichuan-inc/Baichuan2-7B-Base")
+    model_name_or_path: Optional[str] = field(default="openbmb/MiniCPM-2B-sft-bf16")
 
 
 @dataclass
@@ -41,25 +42,21 @@ class TrainingArguments(transformers.TrainingArguments):
 
 class SupervisedDataset(Dataset):
     """Dataset for supervised fine-tuning."""
-
+    
     def __init__(
         self,
         data_path,
         tokenizer,
         model_max_length=4096,
-        user_tokens="<用户>",
-        assistant_tokens="<AI>",
+        user_tokens='<用户>',
+        assistant_tokens='<AI>',
     ):
         super(SupervisedDataset, self).__init__()
         self.data = json.load(open(data_path))
         self.tokenizer = tokenizer
         self.model_max_length = model_max_length
-        self.user_tokens = self.tokenizer.encode(
-            user_tokens
-        )  # 针对不同模型，都可以对应到<用户>的id
-        self.assistant_tokens = self.tokenizer.encode(
-            assistant_tokens
-        )  # 针对不同模型，都可以对应到<AI>的id
+        self.user_tokens = self.tokenizer.encode(user_tokens) #针对不同模型，都可以对应到<用户>的id
+        self.assistant_tokens = self.tokenizer.encode(assistant_tokens) #针对不同模型，都可以对应到<AI>的id
         self.ignore_index = -100
         item = self.preprocessing(self.data[0])
         print("input:", self.tokenizer.decode(item["input_ids"]))
@@ -89,9 +86,10 @@ class SupervisedDataset(Dataset):
                 ] * len(content_ids)
             else:
                 input_ids += self.assistant_tokens + content_ids
-                label_ids += [self.ignore_index] * len(
-                    self.assistant_tokens
-                ) + content_ids
+                label_ids += (
+                    [self.ignore_index] * len(self.assistant_tokens)
+                    + content_ids
+                )
 
         input_ids.append(self.tokenizer.eos_token_id)
         label_ids.append(self.tokenizer.eos_token_id)
@@ -173,7 +171,7 @@ if __name__ == "__main__":
         max_length=training_args.model_max_length,
         use_lora=training_args.use_lora,
         bf16=training_args.bf16,
-        fp16=training_args.fp16,
+        fp16=training_args.fp16
     )
 
     train_dataset = SupervisedDataset(
@@ -197,4 +195,4 @@ if __name__ == "__main__":
 
     trainer.train()
     # save the incremental PEFT weights, more details can be found in https://huggingface.co/blog/peft
-    # model.save_pretrained("output_dir")
+    # model.save_pretrained("output_dir")