change name to minicpm3

modeling_minicpm.py

@@ -997,7 +997,7 @@ MINICPM_START_DOCSTRING = r"""
     "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
     MINICPM_START_DOCSTRING,
 )
-class MiniCPMPreTrainedModel(PreTrainedModel):
+class MiniCPM3PreTrainedModel(PreTrainedModel):
     config_class = MiniCPMConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
@@ -1093,7 +1093,7 @@ MINICPM_INPUTS_DOCSTRING = r"""
     "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
     MINICPM_START_DOCSTRING,
 )
-class MiniCPMModel(MiniCPMPreTrainedModel):
+class MiniCPM3Model(MiniCPM3PreTrainedModel):
     """
     Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
 
@@ -1259,7 +1259,7 @@ class MiniCPMModel(MiniCPMPreTrainedModel):
         )
 
 
-class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
+class MiniCPM3ForCausalLM(MiniCPM3PreTrainedModel):
     _tied_weights_keys = ["lm_head.weight"]
 
     def __init__(self, config):
@@ -1489,7 +1489,7 @@ class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
     """,
     MINICPM_START_DOCSTRING,
 )
-class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
+class MiniCPM3ForSequenceClassification(MiniCPM3PreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels