Update modeling.py

modeling.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 from transformers import PreTrainedModel
+from transformers import LogitsProcessorList, StoppingCriteriaList, MaxLengthCriteria, MinLengthLogitsProcessor
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
 from .configure import RecombinationTransformerConfig
@@ -172,26 +173,12 @@ class RecombinationTransformerForCausalLM(PreTrainedModel):
         
         return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        config = kwargs.pop('config', None)
-        if config is None:
-            config = CustomTransformerConfig.from_pretrained(*args, **kwargs)
-        model = cls(config)
-        state_dict = torch.load(kwargs['pretrained_model_name_or_path'], map_location='cpu')
-        model.load_state_dict(state_dict)
-        return model
-
-    def save_pretrained(self, save_directory):
-        torch.save(self.state_dict(), f"{save_directory}/pytorch_model.bin")
-        self.config.save_pretrained(save_directory)
-
     def generate(self, input_ids, max_length=20, min_length=None, num_return_sequences=1):
         logits_processor = LogitsProcessorList()
         stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])
         
         if min_length is not None:
-            logits_processor.append(MinLengthLogitsProcessor(min_length, eos_token_id=self.config.eos_token_id))
+            logits_processor.append(MinLengthLogitsProcessor(min_length, eos_token_id=config.eos_token_id))
         
         outputs = super().generate(
             input_ids=input_ids,