Update modeling.py

modeling.py

@@ -171,3 +171,34 @@ class RecombinationTransformerForCausalLM(PreTrainedModel):
         logits = self.lm_head(x)
         
         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))
+        
+        outputs = super().generate(
+            input_ids=input_ids,
+            max_length=max_length,
+            num_return_sequences=num_return_sequences,
+            logits_processor=logits_processor,
+            stopping_criteria=stopping_criteria
+        )
+        
+        return outputs