Mismatch error when load int4 model about glm-130b HOT 7 CLOSED

WARNING:torch.distributed.run:
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Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
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WARNING: No training data specified
WARNING: No training data specified
using world size: 2 and model-parallel size: 2 
> padded vocab (size: 150528) with 0 dummy tokens (new size: 150528)
> initializing model parallel with size 2
> Set tokenizer as a icetk-glm-130B tokenizer! Now you can get_tokenizer() everywhere.
Namespace(num_layers=70, hidden_size=12288, num_attention_heads=96, vocab_size=150528, max_sequence_length=2048, layernorm_order='post', inner_hidden_size=32768, hidden_size_per_attention_head=None, model_parallel_size=2, skip_init=True, use_gpu_initialization=False, layernorm_epsilon=1e-05, hidden_dropout=0.1, attention_dropout=0.1, make_vocab_size_divisible_by=128, sandwich_ln=False, experiment_name='MyModel', train_iters=1000000, batch_size=4, lr=0.0001, mode='inference', seed=1234, zero_stage=0, checkpoint_activations=False, checkpoint_num_layers=1, fp16=True, bf16=False, gradient_accumulation_steps=1, epochs=None, log_interval=50, summary_dir='', save_args=False, lr_decay_iters=None, lr_decay_style='linear', lr_decay_ratio=0.1, warmup=0.01, weight_decay=0.01, save=None, load='./glm-130b-sat', save_interval=5000, no_save_rng=False, no_load_rng=False, resume_dataloader=False, distributed_backend='nccl', local_rank=0, exit_interval=None, eval_batch_size=None, eval_iters=100, eval_interval=None, strict_eval=False, train_data=None, train_data_weights=None, iterable_dataset=False, valid_data=None, test_data=None, split='1000,1,1', num_workers=1, block_size=10000, tokenizer_type='icetk-glm-130B', temperature=0.9, top_p=0.0, top_k=1, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, min_tgt_length=0, out_seq_length=256, input_source='input.txt', output_path='samples', with_id=False, max_inference_batch_size=12, device=0, deepspeed=False, deepspeed_config=None, deepscale=False, deepscale_config=None, deepspeed_mpi=False, cuda=True, rank=0, world_size=2, master_ip='127.0.0.1', master_port='29500', bminf=False, bminf_memory_limit=44, quantization_bit_width=4, from_quantized_checkpoint=True, sequential_initialization=True, sampling_strategy='BeamSearchStrategy', min_gen_length=0, print_all_beams=False, do_train=False)
> Quantizing model weight to 4 bits
global rank 0 is loading checkpoint ./glm-130b-sat/49300/mp_rank_00_model_states.pt
Traceback (most recent call last):
  File "/ssd1/xingyum/GLM-130B/generate.py", line 210, in <module>
    main(args)
  File "/ssd1/xingyum/GLM-130B/generate.py", line 156, in main
    model, tokenizer = initialize_model_and_tokenizer(args)
  File "/ssd1/xingyum/GLM-130B/initialize.py", line 72, in initialize_model_and_tokenizer
    load_checkpoint(model, args)
  File "/home/xingyum/anaconda3/envs/vis/lib/python3.10/site-packages/SwissArmyTransformer/training/model_io.py", line 181, in load_checkpoint
    missing_keys, unexpected_keys = module.load_state_dict(sd['module'], strict=False)
  File "/home/xingyum/anaconda3/envs/vis/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for GLM130B:
        size mismatch for transformer.word_embeddings.weight: copying a param with shape torch.Size([18816, 12288]) from checkpoint, the shape in current model is torch.Size([75264, 12288]).
        size mismatch for transformer.layers.0.attention.query_key_value.bias: copying a param with shape torch.Size([4608]) from checkpoint, the shape in current model is torch.Size([18432]).
        size mismatch for transformer.layers.0.attention.query_key_value.weight: copying a param with shape torch.Size([4608, 12288]) from checkpoint, the shape in current model is torch.Size([18432, 6144]).

size mismatch for transformer.layers.69.mlp.dense_h_to_4h.weight: copying a param with shape torch.Size([16384, 12288]) from checkpoint, the shape in current model is torch.Size([32768, 6144]).