> python quantize.py --checkpoint_path checkpoints/$MODEL_REPO/model.pth --mode int4 --groupsize 64Loading model ...Quantizing model weights for int4 weight-only affine per-channel groupwise quantizationlinear: layers.0.attention.wqkv, in=4096, out=6144Traceback (most recent call last):  File "/data/users/jerryzh/gpt-fast/quantize.py", line 622, in <module>    quantize(args.checkpoint_path, args.mode, args.groupsize, args.calibration_tasks, args.calibration_limit, args.calibration_seq_length, args.pad_calibration_inputs, args.percdamp, args.blocksize, args.label)  File "/data/users/jerryzh/gpt-fast/quantize.py", line 569, in quantize    quantized_state_dict = quant_handler.create_quantized_state_dict()  File "/home/jerryzh/.conda/envs/sglang/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context    return func(*args, **kwargs)  File "/data/users/jerryzh/gpt-fast/quantize.py", line 433, in create_quantized_state_dict    weight_int4pack, scales_and_zeros = prepare_int4_weight_and_scales_and_zeros(  File "/data/users/jerryzh/gpt-fast/quantize.py", line 363, in prepare_int4_weight_and_scales_and_zeros    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(weight_int32, inner_k_tiles)  File "/home/jerryzh/.conda/envs/sglang/lib/python3.10/site-packages/torch/_ops.py", line 1123, in __call__    return self._op(*args, **(kwargs or {}))RuntimeError: Expected in.dtype() == at::kByte to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)