def init_teacher_model():
    # model = MiniMindLM(lm_config)
    # moe_path = '_moe' if lm_config.use_moe else ''
    # ckp = args.mlm_pretrained_model_path
    vocab_path = args.arg_overrides['data'] + '/dict.txt'
    tokenizer = Dictionary.load(vocab_path)
    tokenizer.add_symbol('<mask>')

    model_pre = load_pretrained_ernierna(args.mlm_pretrained_model_path, args.arg_overrides)
    model = model_pre.encoder

    if args.debug:
        print('debug mode')
        num_layers_to_keep = 1  # 保留12层,todo
        model.sentence_encoder.layers = model.sentence_encoder.layers[
                                                            :num_layers_to_keep]
        # torch.save(model,args.save_dir+'/pretraining0215.pt')
        # print('save small ERNIE-RNA model in',args.save_dir+'/pretraining0215.pt')
        # state_dict = torch.load(ckp, map_location=args.device)
        # model.load_state_dict(state_dict, strict=False)
    Logger(f'教师模型(LLM)总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万, vocab_size={len(tokenizer)}')
    model = model.to(args.device)
    print(model)
    return model,tokenizer